{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5e03085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8905196332424647 0.8798865822018248\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  实验2-1\n",
    "# 批梯度下降\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd \n",
    "import matplotlib.pyplot as plt\n",
    "# load dataset\n",
    "df = pd.read_csv('temperature_dataset.csv')\n",
    "data = np.array(df)\n",
    "y0 = np.array([i[0] for i in data]) # 第一列作为样本标注\n",
    "y14 = np.array([i[1:] for i in data])#2-5 作为四维输出特征\n",
    "xuexi = 0.0001 # 学习率\n",
    "ep = 20\n",
    "epoch = [ep] # 遍历次数\n",
    "m =  np.size(data,0) # 获取样本长度 m\n",
    "alls = [i for i in range(m)]\n",
    "trainsyo = [] # 训练集标注\n",
    "testyo = [] # 测试集标注\n",
    "trainsy14 = [] # 训练集输入特征\n",
    "testy14 = [] # 测试集输入特征\n",
    "tra = int(m * 0.8) # 训练集长度\n",
    "tes = m - tra # 测试集长度\n",
    "for i in range(tra): # 获取训练集\n",
    "    a = rd.choice(alls) # 从alls中随机选取一个\n",
    "    trainsyo.append(y0[a]) # 增加训练集标注\n",
    "    trainsy14.append(y14[a])#  增加训练集输入特征\n",
    "    alls.remove(a)\n",
    "trainsy14 = np.array(trainsy14) # 训练集list转变array\n",
    "trainsyo = np.array(trainsyo) # 训练集list转变array\n",
    "for a in alls: # 获取测试集\n",
    "#     global y0\n",
    "#     global y14\n",
    "    testyo.append(y0[a])# 增加测试集标注\n",
    "    testy14.append(y14[a]) # 增加测试集输入特征\n",
    "w = np.array([rd.random() for i in range(4)]) # 初次随机获取权重w\n",
    "# print(w)\n",
    "b = [rd.random()] # 初次随机获取偏差b\n",
    "RMSE = [] # 均方根误差\n",
    "RMSE2  = [] # 训练集\n",
    "while(epoch[0]): # 开始遍历\n",
    "    epoch[0]  -=  1 # 设置的超参数epoch -1\n",
    "    e =np.dot(testy14,w.transpose()) + b[0] - testyo\n",
    "    RMSE.append((np.dot(e,e.transpose())/tes)**0.5)\n",
    "    e = np.dot(trainsy14,w.transpose()) + b[0] - trainsyo # n*1\n",
    "    RMSE2.append((np.dot(e,e.transpose())/tra)**0.5)\n",
    "    b[0] -= 2*xuexi*np.dot(np.ones(tra),e)/tra\n",
    "    w -= 2*xuexi*np.dot(e.transpose(),trainsy14)/tra\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "x = [i+1 for i in range(ep)] # 设置x坐标 \n",
    "print(min(RMSE),min(RMSE2))\n",
    "ymax = max(RMSE) + 1\n",
    "plt.xlabel('epoch') # 设置x坐标名称\n",
    "plt.ylabel('RMSE') # 设置y坐标名称\n",
    "plt.title('训练情况') # 设置标题\n",
    "plt.plot(x,RMSE,color='r',marker='o',linestyle='dashed')\n",
    "# plt.plot(x,RMSE,color='r') # 设置绘图的基本参数\n",
    "plt.axis([0,ep+ 1,0,ymax]) # 设置xy的取值范围\n",
    "plt.show() # 展示图片\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e736ce25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd \n",
    "import matplotlib.pyplot as plt\n",
    "# load dataset\n",
    "df = pd.read_csv('temperature_dataset.csv')\n",
    "data = np.array(df)\n",
    "y0 = np.array([i[0] for i in data]) # 第一列作为样本标注\n",
    "y14 = np.array([i[3] for i in data])# 选第三维作为输入特征\n",
    "xuexi = 0.0001 # 学习率\n",
    "ep = 20\n",
    "epoch = [ep] # 遍历次数\n",
    "m =  np.size(data,0) # 获取样本长度 m\n",
    "alls = [i for i in range(m)]\n",
    "trainsyo = [] # 训练集标注\n",
    "testyo = [] # 测试集标注\n",
    "trainsy14 = [] # 训练集输入特征\n",
    "testy14 = [] # 测试集输入特征\n",
    "tra = int(m * 0.8) # 训练集长度\n",
    "tes = m - tra # 测试集长度\n",
    "allw = [] # 训练过程中的全部的w\n",
    "allb = [] # 训练过程中的全部的b\n",
    "for i in range(tra): # 获取训练集\n",
    "    a = rd.choice(alls) # 从alls中随机选取一个\n",
    "    trainsyo.append(y0[a]) # 增加训练集标注\n",
    "    trainsy14.append(y14[a])#  增加训练集输入特征\n",
    "    alls.remove(a)\n",
    "trainsy14 = np.array(trainsy14) # 训练集list转变array\n",
    "trainsyo = np.array(trainsyo) # 训练集list转变array\n",
    "for a in alls: # 获取测试集\n",
    "#     global y0\n",
    "#     global y14\n",
    "    testyo.append(y0[a])# 增加测试集标注\n",
    "    testy14.append(y14[a]) # 增加测试集输入特征\n",
    "w = np.array([rd.random() for i in range(1)]) # 初次随机获取权重w\n",
    "# print(w)\n",
    "b = [rd.random()] # 初次随机获取偏差b\n",
    "RMSE = [] # 均方根误差\n",
    "while(epoch[0]): # 开始遍历\n",
    "    epoch[0]  -=  1 # 设置的超参数epoch -1\n",
    "    newb = 0 # 求解的b的值\n",
    "    newW = int() # 求解的w的值\n",
    "    for i in range(tra): # 遍历全部的训练集\n",
    "        a = np.dot(w,trainsy14[i].transpose()) + b[0] - trainsyo[i] # 计算(W*xT + b - y )\n",
    "        newb += float(a) # 累加\n",
    "        if type(newW) == int: \n",
    "            newW = trainsy14[i].transpose()*float(a) # 初次赋值\n",
    "        else:\n",
    "            newW += trainsy14[i].transpose()*float(a) # 累加w\n",
    "    w -= xuexi*newW * (2/tra) # 更新w\n",
    "    b[0] -= xuexi*newb *2 / tra # 更新b\n",
    "    allw.append(w[0])\n",
    "    allb.append(b[0])\n",
    "yall = []\n",
    "xs= [-5,-3,--1,0,1,3,5] # x 轴模板\n",
    "xall = [xs for i in range(ep)]\n",
    "ymax = 0\n",
    "ymin = 30\n",
    "for i in range(ep):\n",
    "    yall.append([allw[i]*t + allb[i] for t in xs])\n",
    "    ymax = max(ymax,max(yall[i]))\n",
    "    ymin  = min(ymin,min(yall[i]))\n",
    "#绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "x = [i+1 for i in range(ep)] # 设置x坐标 \n",
    "plt.xlabel('x') # 设置x坐标名称\n",
    "plt.ylabel('y') # 设置y坐标名称\n",
    "plt.title('拟合直线变化') # 设置标题\n",
    "plots = []\n",
    "for i in range(ep):\n",
    "    c='r'\n",
    "    if i == 0:\n",
    "        c = 'b'\n",
    "    elif i == ep -1:\n",
    "        c = 'k'\n",
    "    p, = plt.plot(xall[i],yall[i],color=c,marker='o',linestyle='dashed')\n",
    "    plots.append(p)\n",
    "# plt.plot(x,RMSE,color='r') # 设置绘图的基本参数\n",
    "plt.legend((plots[0],plots[-1]),['start','end'])\n",
    "plt.axis([-6,6,ymin-1,ymax+1]) # 设置xy的取值范围\n",
    "plt.show() # 展示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9989c4f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.075553184670219\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-3\n",
    "# 随机梯度下降\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd \n",
    "import matplotlib.pyplot as plt\n",
    "# load dataset\n",
    "np.random.seed(100)\n",
    "rng = np.random.default_rng()\n",
    "df = pd.read_csv('temperature_dataset.csv')\n",
    "data = np.array(df)\n",
    "y0 = np.array([i[0] for i in data]) # 第一列作为样本标注\n",
    "y14 = np.array([i[1:] for i in data])#2-5 作为四维输出特征\n",
    "xuexi = 0.0001 # 学习率\n",
    "ep = 200\n",
    "epoch = [ep] # 遍历次数\n",
    "m =  np.size(data,0) # 获取样本长度 m\n",
    "alls = [i for i in range(m)]\n",
    "trainsyo = [] # 训练集标注\n",
    "testyo = [] # 测试集标注\n",
    "trainsy14 = [] # 训练集输入特征\n",
    "testy14 = [] # 测试集输入特征\n",
    "tra = int(m * 0.8) # 训练集长度\n",
    "tes = m - tra # 测试集长度\n",
    "for i in range(tra): # 获取训练集\n",
    "    a = rd.choice(alls) # 从alls中随机选取一个\n",
    "    trainsyo.append(y0[a]) # 增加训练集标注\n",
    "    trainsy14.append(y14[a])#  增加训练集输入特征\n",
    "    alls.remove(a)\n",
    "trainsy14 = np.array(trainsy14) # 训练集list转变array\n",
    "trainsyo = np.array(trainsyo) # 训练集list转变array\n",
    "for a in alls: # 获取测试集\n",
    "#     global y0\n",
    "#     global y14\n",
    "    testyo.append(y0[a])# 增加测试集标注\n",
    "    testy14.append(y14[a]) # 增加测试集输入特征\n",
    "w = np.array([rd.random() for i in range(4)]) # 初次随机获取权重w\n",
    "# print(w)\n",
    "b = [rd.random()] # 初次随机获取偏差b\n",
    "RMSE = [] # 均方根误差\n",
    "while(epoch[0]): # 开始遍历\n",
    "    epoch[0]  -=  1 # 设置的超参数epoch -1\n",
    "    newtra = [i for i in range(tra)]\n",
    "    rd.shuffle(newtra)\n",
    "    i = rd.choice(newtra)\n",
    "    a = np.dot(w,trainsy14[i].transpose()) + b[0] - trainsyo[i] # 计算(W*xT + b - y )\n",
    "    b[0] -=  2*xuexi*a\n",
    "    w  -= 2*xuexi*trainsy14[i].transpose()*a # 初次赋值\n",
    "    y = 0 # 中间变量用于存储RMSE每一轮的\n",
    "    for i in range(tes): # \n",
    "        y += (np.dot(w,testy14[i].transpose()) + b[0] - testyo[i])**2 # 计算均方根误差，并累加 \n",
    "    RMSE.append((y/tes)**0.5)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "x = [i+1 for i in range(ep)] # 设置x坐标 \n",
    "print(min(RMSE))\n",
    "ymax = max(RMSE) + 1\n",
    "plt.xlabel('epoch') # 设置x坐标名称\n",
    "plt.ylabel('RMSE') # 设置y坐标名称\n",
    "plt.title('训练情况') # 设置标题\n",
    "plt.plot(x,RMSE,color='r',marker='o',linestyle='dashed')\n",
    "# plt.plot(x,RMSE,color='r') # 设置绘图的基本参数\n",
    "plt.axis([0,ep+ 1,0,ymax]) # 设置xy的取值范围\n",
    "plt.show() # 展示图片\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01336635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8884755958553092\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-4\n",
    "# 小批梯度下降\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd \n",
    "import matplotlib.pyplot as plt\n",
    "# load dataset\n",
    "df = pd.read_csv('temperature_dataset.csv')\n",
    "data = np.array(df)\n",
    "y0 = np.array([i[0] for i in data]) # 第一列作为样本标注\n",
    "y14 = np.array([i[1:] for i in data])#2-5 作为四维输出特征\n",
    "xuexi = 0.0001 # 学习率\n",
    "batch = 30 # 设置批长度\n",
    "ep = 20 # 设置遍历次数\n",
    "epoch = [ep] # 遍历次数\n",
    "m =  np.size(data,0) # 获取样本长度 m\n",
    "alls = [i for i in range(m)]\n",
    "trainsyo = [] # 训练集标注\n",
    "testyo = [] # 测试集标注\n",
    "trainsy14 = [] # 训练集输入特征\n",
    "testy14 = [] # 测试集输入特征\n",
    "tra = int(m * 0.8) # 训练集长度\n",
    "tes = m - tra # 测试集长度\n",
    "for i in range(tra): # 获取训练集\n",
    "    a = rd.choice(alls) # 从alls中随机选取一个\n",
    "    trainsyo.append(y0[a]) # 增加训练集标注\n",
    "    trainsy14.append(y14[a])#  增加训练集输入特征\n",
    "    alls.remove(a)\n",
    "trainsy14 = np.array(trainsy14) # 训练集list转变array\n",
    "trainsyo = np.array(trainsyo) # 训练集list转变array\n",
    "for a in alls: # 获取测试集\n",
    "#     global y0\n",
    "#     global y14\n",
    "    testyo.append(y0[a])# 增加测试集标注\n",
    "    testy14.append(y14[a]) # 增加测试集输入特征\n",
    "w = np.array([rd.random() for i in range(4)]) # 初次随机获取权重w\n",
    "# print(w)\n",
    "b = [rd.random()] # 初次随机获取偏差b\n",
    "RMSE2 = []\n",
    "RMSE = [] # 均方根误差\n",
    "while(epoch[0]): # 开始遍历\n",
    "    epoch[0]  -=  1 # 设置的超参数epoch -1\n",
    "    if not batch:break\n",
    "    times = tra // batch\n",
    "    if tra % batch:\n",
    "        times += 1\n",
    "    for _ in range(times):\n",
    "        starts = _*batch\n",
    "        ends = (_+1)*batch\n",
    "        if (_+1)*batch >= tra:\n",
    "            ends = tra\n",
    "            batch = tra%batch\n",
    "            if(not batch):break\n",
    "        e =np.dot(testy14,w.transpose()) + b[0] - testyo\n",
    "        RMSE.append((np.dot(e,e.transpose())/tes)**0.5)\n",
    "        e = np.dot(trainsy14[starts:ends],w.transpose()) + b[0] - trainsyo[starts:ends] # n*1\n",
    "        RMSE2.append((np.dot(e,e.transpose())/batch)**0.5)\n",
    "        b[0] -= 2*xuexi*np.dot(np.ones(batch),e)/batch\n",
    "        w -= 2*xuexi*np.dot(e.transpose(),trainsy14[starts:ends])/batch\n",
    "# 绘图部分\n",
    "print(min(RMSE))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "x = [i+1 for i in range(20)] # 设置x坐标 \n",
    "RMSE = RMSE[:20]\n",
    "ymax = max(RMSE[:20]) + 1\n",
    "plt.xlabel('epoch') # 设置x坐标名称\n",
    "plt.ylabel('RMSE') # 设置y坐标名称\n",
    "plt.title('训练情况') # 设置标题\n",
    "plt.plot(x,RMSE,color='r',marker='o',linestyle='dashed')\n",
    "# plt.plot(x,RMSE,color='r') # 设置绘图的基本参数\n",
    "plt.axis([-2,len(RMSE)+ 6,0,ymax+3]) # 设置xy的取值范围\n",
    "plt.show() # 展示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fce835da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8616024339891946 0.8957073347132285\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2-5 \n",
    "# 各种标准化\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd \n",
    "import matplotlib.pyplot as plt\n",
    "# load dataset\n",
    "df = pd.read_csv('temperature_dataset.csv')\n",
    "data = np.array(df)\n",
    "y0 = np.array([i[0] for i in data]) # 第一列作为样本标注\n",
    "y14 = np.array([i[1:] for i in data])#2-5 作为四维输出特征\n",
    "# 标准化\n",
    "# mean = np.mean(y14,0) # 平均值\n",
    "# stds = np.std(y14,0,ddof=1)\n",
    "# y14 = (y14-mean)/stds\n",
    "# 最小最大归一化\n",
    "# maxs = np.amax(y14,0)\n",
    "# mins = np.amin(y14,0)\n",
    "# y14 = (y14 - mins)/(maxs-mins)\n",
    "# 均值归一化\n",
    "mean = np.mean(y14,0) # 平均值\n",
    "maxs = np.amax(y14,0)\n",
    "mins = np.amin(y14,0)\n",
    "y14 = (y14-mean)/(maxs - mins)\n",
    "xuexi = 0.1 # 学习率\n",
    "ep = 2000\n",
    "epoch = [ep] # 遍历次数\n",
    "m =  np.size(data,0) # 获取样本长度 m\n",
    "alls = [i for i in range(m)]\n",
    "trainsyo = [] # 训练集标注\n",
    "testyo = [] # 测试集标注\n",
    "trainsy14 = [] # 训练集输入特征\n",
    "testy14 = [] # 测试集输入特征\n",
    "tra = int(m * 0.8) # 训练集长度\n",
    "tes = m - tra # 测试集长度\n",
    "for i in range(tra): # 获取训练集\n",
    "    a = rd.choice(alls) # 从alls中随机选取一个\n",
    "    trainsyo.append(y0[a]) # 增加训练集标注\n",
    "    trainsy14.append(y14[a])#  增加训练集输入特征\n",
    "    alls.remove(a)\n",
    "trainsy14 = np.array(trainsy14) # 训练集list转变array\n",
    "trainsyo = np.array(trainsyo) # 训练集list转变array\n",
    "for a in alls: # 获取测试集\n",
    "    testyo.append(y0[a])# 增加测试集标注\n",
    "    testy14.append(y14[a]) # 增加测试集输入特征\n",
    "# w = np.array([rd.random() for i in range(4)]) # 初次随机获取权重w\n",
    "w =np.array([0.,0.,0.,0.])\n",
    "# print(w)\n",
    "# b = [rd.random()] # 初次随机获取偏差b\n",
    "b = [0.]\n",
    "RMSE = [] # 均方根误差\n",
    "RMSE2 = [] # 训练集均方根误差\n",
    "while(epoch[0]): # 开始遍历\n",
    "    epoch[0]  -=  1 # 设置的超参数epoch -1\n",
    "    e =np.dot(testy14,w.transpose()) + b[0] - testyo\n",
    "    RMSE.append((np.dot(e,e.transpose())/tes)**0.5)\n",
    "    e = np.dot(trainsy14,w.transpose()) + b[0] - trainsyo # n*1\n",
    "    RMSE2.append((np.dot(e,e.transpose())/tra)**0.5)\n",
    "    b[0] -= 2*xuexi*np.dot(np.ones(tra),e)/tra\n",
    "    w -= 2*xuexi*np.dot(e.transpose(),trainsy14)/tra\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "x = [i+1 for i in range(ep)] # 设置x坐标 \n",
    "print(min(RMSE),min(RMSE2))\n",
    "ymax = max(RMSE) + 1\n",
    "plt.xlabel('epoch') # 设置x坐标名称\n",
    "plt.ylabel('RMSE') # 设置y坐标名称\n",
    "plt.title('训练情况') # 设置标题\n",
    "plt.plot(x,RMSE,color='r',linestyle='dashed')\n",
    "plt.plot(x,RMSE2,color='k',linestyle='dashed')\n",
    "# plt.plot(x,RMSE,color='r') # 设置绘图的基本参数\n",
    "plt.axis([0,ep+ 1,0,ymax]) # 设置xy的取值范围\n",
    "plt.show() # 展示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3dfbeb99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-8\n",
    "# 线性回归对百分制成绩进行二分类\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random as rd\n",
    "# parameters\n",
    "dataset = 2 # index of training dataset\n",
    "epoch = 2000\n",
    "sdy = 0.0001\n",
    "# datasets for training\n",
    "if dataset == 1: # balanced dataset\n",
    "    x_train = np.array([50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]).reshape((1, -1))\n",
    "    y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape((1, -1))\n",
    "elif dataset == 2: # unbalanced dataset 1\n",
    "    x_train = np.array([0, 5, 10, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70]).reshape((1, -1))\n",
    "    y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape((1, -1))\n",
    "elif dataset == 3: # unbalanced dataset 2\n",
    "    x_train = np.array([0, 5, 10, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]).reshape((1, -1))\n",
    "    y_train = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape((1, -1))\n",
    "m_train = x_train.size # number of training examples\n",
    "w = np.array([0.]).reshape((1, -1))# 权重\n",
    "b = np.array([0.])# 偏差\n",
    "# 批梯度下降\n",
    "for _  in range(epoch):\n",
    "    e = np.dot(w,x_train) + b - y_train\n",
    "#     print(e)\n",
    "    b -= 2*sdy*np.dot(e,np.ones(m_train))/m_train\n",
    "    w -= 2*sdy*np.dot(x_train,e.transpose())/m_train\n",
    "# 画图部分\n",
    "yc = []\n",
    "yy = []\n",
    "lines = w[0][0]*60 + b[0] \n",
    "for i in x_train[0]:\n",
    "    datas = w[0][0]*i + b[0]\n",
    "    yy.append(datas)\n",
    "    yc.append( 1 if datas >=  lines  else 0)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "plt.xlabel('分数')\n",
    "plt.ylabel('结果')\n",
    "plt.plot(x_train[0],yy)\n",
    "plt.scatter(x_train[0],yc)\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "226e56bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09246259191957222\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-9\n",
    "# 均方误差代价函数->逻辑回归模型\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "# load dataset\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "y4 = np.array([i[5:] for i in data]) # 标注\n",
    "y03 = np.array([i[:5] for i in data]) # 4维输入特征\n",
    "# 标准化\n",
    "mean = np.mean(y03,0) # 平均值\n",
    "stds = np.std(y03,0,ddof=1) # 标准差\n",
    "y03 = (y03-mean)/stds# 标准化完成\n",
    "# 学习率\n",
    "xuexi = 0.1\n",
    "# 训练次数\n",
    "epoch = 30000\n",
    "# 区分训练集和测试集\n",
    "m = len(data) # 样本总长度\n",
    "__ = [i for i in range(m)]\n",
    "tram = int(m*0.7) # 训练集长度\n",
    "tesm = m - tram # 测试集长度\n",
    "trainx = [] # 训练集输入特征\n",
    "trainy = [] # 训练集标注\n",
    "testx = [] # 测试集输入特征\n",
    "testy = [] # 测试集标注\n",
    "# 开始随机筛选训练集\n",
    "for i in range(tram):\n",
    "    _ = rd.choice(__)\n",
    "    trainx.append(y03[_])\n",
    "    trainy.append(y4[_])\n",
    "    __.remove(_)\n",
    "# 剩下的样本就是 测试集合\n",
    "for i in __:\n",
    "    testx.append(y03[i])\n",
    "    testy.append(y4[i])\n",
    "trainx,trainy,testx,testy= np.array(trainx),np.array(trainy),np.array(testx),np.array(testy) # list => np.array\n",
    "# 设置权重w，和偏差b\n",
    "w = np.array([0.,0.,0.,0.,0.]).reshape(1,-1)\n",
    "b = np.array([0.]).reshape(1,-1)\n",
    "REMS = []\n",
    "for _ in range(epoch):\n",
    "    y = -(np.dot(w,trainx.transpose()) + b) # -wTx + b,1*n\n",
    "    y = 1/(1 + np.exp(y)) # 计算1/(1+e(-(wTx+b)))1*n \n",
    "    e = y - trainy.transpose() # 计算 e  1*n\n",
    "    w -= 2*xuexi*np.dot((y*(1-y)*e),trainx)/tram # 1*4 # 更新w\n",
    "    b -= 2*xuexi*np.dot((y*(1-y)),e.transpose()) / tram # 更新b\n",
    "    # 测试集REMS 计算\n",
    "    y = -(np.dot(w,testx.transpose()) + b) # -wTx + b,1*n \n",
    "    y = 1/(1 + np.exp(y)) # 1*n \n",
    "    e = y - testy.transpose() # 1*n\n",
    "    a = np.dot(e,e.transpose())[0][0]/tesm\n",
    "    REMS.append(a**0.5)\n",
    "\n",
    "    # 绘图部分\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "x = [i for i in range(epoch)]\n",
    "print(min(REMS))\n",
    "plt.plot(x,REMS,color='b',linestyle='dashed')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('REMS')\n",
    "plt.title('训练情况')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cff70ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigmoid函数图像\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "x_nums = [rd.uniform(-10,10) for i in range(1000)]\n",
    "x_nums.sort()\n",
    "y_nums = [1/(1+np.e**(-i)) for i in x_nums]\n",
    "# 画图\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "plt.plot(x_nums,y_nums)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "144ef878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.  -2.5  0.   2.5  5. ]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3D图像绘画\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# 生成数据\n",
    "x = np.linspace(-5, 5, 5)\n",
    "y = np.linspace(-5, 5, 5)\n",
    "print(x)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = np.sin(np.sqrt(X**2 + Y**2))\n",
    "print(type(X))\n",
    "# 创建3D图形对象\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# 绘制3D曲面图\n",
    "ax.plot_surface(X, Y, Z, cmap='viridis')\n",
    "\n",
    "# 设置图形属性\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "ax.set_title('3D Surface Plot')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c2c88bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3D图像展示2-9\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "# load dataset\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "y4 = np.array([i[5:] for i in data]) # 标注\n",
    "y03 = np.array([i[:1] for i in data]) # 1维输入特征\n",
    "# 标准化\n",
    "mean = np.mean(y03,0) # 平均值\n",
    "stds = np.std(y03,0,ddof=1) # 标准差\n",
    "y03 = (y03-mean)/stds# 标准化完成\n",
    "# 学习率\n",
    "xuexi = 0.1\n",
    "# 训练次数\n",
    "epoch = 1000\n",
    "# 区分训练集和测试集\n",
    "m = len(data) # 样本总长度\n",
    "__ = [i for i in range(m)]\n",
    "tram = int(m*0.7) # 训练集长度\n",
    "tesm = m - tram # 测试集长度\n",
    "trainx = [] # 训练集输入特征\n",
    "trainy = [] # 训练集标注\n",
    "testx = [] # 测试集输入特征\n",
    "testy = [] # 测试集标注\n",
    "# 开始随机筛选训练集\n",
    "for i in range(tram):\n",
    "    _ = rd.choice(__)\n",
    "    trainx.append(y03[_])\n",
    "    trainy.append(y4[_])\n",
    "    __.remove(_)\n",
    "# 剩下的样本就是 测试集合\n",
    "for i in __:\n",
    "    testx.append(y03[i])\n",
    "    testy.append(y4[i])\n",
    "trainx,trainy,testx,testy= np.array(trainx),np.array(trainy),np.array(testx),np.array(testy) # list => np.array\n",
    "# 设置权重w，和偏差b\n",
    "w = np.array([10.]).reshape(1,-1)\n",
    "b = np.array([0.]).reshape(1,-1)\n",
    "REMS = []\n",
    "Wchange=[] # w的变化过程\n",
    "Bchange=[] # B的变化过程\n",
    "for _ in range(epoch):\n",
    "    y = -(np.dot(w,trainx.transpose()) + b) # -wTx + b,1*n\n",
    "    y = 1/(1 + np.exp(y)) # 计算1/(1+e(-(wTx+b)))1*n \n",
    "    e = y - trainy.transpose() # 计算 e  1*n\n",
    "    w -= 2*xuexi*np.dot((y*(1-y)*e),trainx)/tram # 1*4 # 更新w\n",
    "    b -= 2*xuexi*np.dot((y*(1-y)),e.transpose()) / tram # 更新b\n",
    "    # 测试集REMS 计算\n",
    "    y = -(np.dot(w,testx.transpose()) + b) # -wTx + b,1*n \n",
    "    y = 1/(1 + np.exp(y)) # 1*n \n",
    "    e = y - testy.transpose() # 1*n\n",
    "    a = np.dot(e,e.transpose())[0][0]/tesm\n",
    "    REMS.append(a**0.5)\n",
    "    Wchange.append(w[0][0])\n",
    "    Bchange.append(b[0][0])\n",
    "\n",
    "    # 绘图部分\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "Wchange, Bchange=np.meshgrid(Wchange,Bchange)\n",
    "_ = REMS[:]\n",
    "REMS = np.array([_ for i in range(epoch)])\n",
    "# 绘制3D曲面图\n",
    "ax.plot_surface(Bchange, Wchange, REMS, cmap='viridis')\n",
    "\n",
    "# 设置图形属性\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "ax.set_title('3D Surface Plot')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8bfd214b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(268, 5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 实验2-10\n",
    "# 交叉熵代价函数\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "# 基本参数确定\n",
    "epoch = 10000\n",
    "xuexi = 0.1\n",
    "# 加载数据\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "# 训练集和测试集长度确定\n",
    "split_rate = 0.7\n",
    "train_m = int(len(data)*split_rate)\n",
    "test_m = len(data) - train_m\n",
    "d = len(data[0])-1 # 输入特征维度（从0开始计数）\n",
    "# 标准化\n",
    "mean = np.mean(data[:train_m,0:d],0) # 平均值\n",
    "stds = np.std(data[:train_m,0:d],0,ddof=1)# 标准差\n",
    "data[:,0:d]  = (data[:,0:d]-mean)/stds\n",
    "# 制作训练集和测试集\n",
    "print(type(data))\n",
    "train_X,train_Y, test_X,test_Y= data[:train_m,0:d],data[:train_m,d:],data[train_m:,0:d], data[train_m:,d:]\n",
    "print(data[:train_m,0:d].shape)\n",
    "# 初始化权重w和偏差b\n",
    "w ,b= np.random.rand(1,d),np.random.rand(1,1)\n",
    "Data = []\n",
    "def Confusion_matrix(a,b): # 计算召回率，精度，F!分数的函数\n",
    "    b = b > 0.5\n",
    "    allp = np.sum(b)\n",
    "    c = a*b\n",
    "    TP = np.sum(c)\n",
    "    FP = np.sum(a*(1-c))\n",
    "#     FN = np.sum(b*(1-c))\n",
    "#     TN = train_m - FP-allp\n",
    "    R = TP/allp\n",
    "    P = TP/(FP+TP)\n",
    "    return R,P,2/(1/R + 1/P)\n",
    "for _A_ in range(epoch):\n",
    "    # 利用代价函数更新 w和 b\n",
    "    e = -(np.dot(w,train_X.transpose())+ b)\n",
    "    e = 1/(1 + np.exp(e)) \n",
    "    e -=  train_Y.transpose() # 1*train_m\n",
    "    w -= xuexi*np.dot(e,train_X)/train_m\n",
    "    b -= xuexi*np.dot(e,np.ones(train_m).transpose())/train_m\n",
    "    # 计算交叉熵和召回率，精度以及F1分数\n",
    "    # 训练集\n",
    "    e = -(np.dot(w,train_X.transpose())+ b)\n",
    "    N_Y = 1/(1 + np.exp(e))\n",
    "    J = np.dot(np.log(N_Y),train_Y) +np.dot(np.log(1-N_Y),(1-train_Y)) \n",
    "    R,P,F1 = Confusion_matrix(N_Y>=0.5,train_Y[:].transpose())\n",
    "    _ = {\"R\":R,\"P\":P,\"F1\":F1,'J1':-J[0][0]/train_m}\n",
    "    # 测试集\n",
    "    e =  -(np.dot(w,test_X.transpose())+ b)\n",
    "    N_Y = 1/(1 + np.exp(e))\n",
    "    J = np.dot(np.log(N_Y),test_Y) +np.dot(np.log(1-N_Y),(1-test_Y))\n",
    "    _['J2'] = -J[0][0]/test_m\n",
    "    _['R1'],_['P1'],_['F11'] = Confusion_matrix(N_Y>=0.5,test_Y[:].transpose())\n",
    "    Data.append(_)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "fig, axes = plt.subplots(2, 2)\n",
    "x = [i for i in range(epoch)]\n",
    "axes[0][0].plot(x,[i['J1'] for i in Data],color='r',linestyle='dashed',label='train')\n",
    "axes[0][0].plot(x,[i['J2'] for i in Data],color='b',linestyle='dashed',label='test')\n",
    "axes[0][0].legend()\n",
    "axes[0][0].set_title('交叉熵')\n",
    "axes[0][1].plot(x,[i['P'] for i in Data],color='r',linestyle='dashed',label='train')\n",
    "axes[0][1].plot(x,[i['P1'] for i in Data],color='b',linestyle='dashed',label='test')\n",
    "axes[0][1].legend()\n",
    "axes[0][1].set_title('精度')\n",
    "axes[1][0].plot(x,[i['F1'] for i in Data],color='r',linestyle='dashed',label='train')\n",
    "axes[1][0].plot(x,[i['F11'] for i in Data],color='b',linestyle='dashed',label='test')\n",
    "axes[1][0].set_title('F1分数')\n",
    "axes[1][0].legend()\n",
    "axes[1][1].plot(x,[i['R'] for i in Data],color='r',linestyle='dashed',label='train')\n",
    "axes[1][1].plot(x,[i['R1'] for i in Data],color='b',linestyle='dashed',label='test')\n",
    "axes[1][1].set_title('召回率')\n",
    "axes[1][1].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3cdf6fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-11\n",
    "# scikit-learn支持向量机\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "from sklearn import svm\n",
    "# 基本参数确定\n",
    "epoch = 10000\n",
    "xuexi = 0.1\n",
    "# 加载数据\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "# 训练集和测试集长度确定\n",
    "split_rate = 0.7\n",
    "train_m = int(len(data)*split_rate)\n",
    "test_m = len(data) - train_m\n",
    "d = len(data[0])-1 # 输入特征维度（从0开始计数）\n",
    "# 标准化\n",
    "mean = np.mean(data[:train_m,0:d],0) # 平均值\n",
    "stds = np.std(data[:train_m,0:d],0,ddof=1)# 标准差\n",
    "data[:,0:d]  = (data[:,0:d]-mean)/stds\n",
    "data[:,d:] = data[:,d:]+(data[:,d:]-1) # 将标注0改为1\n",
    "# 制作训练集和测试集\n",
    "train_X,train_Y, test_X,test_Y= data[:train_m,0:d],data[:train_m,d:],data[train_m:,0:d], data[train_m:,d:]\n",
    "Call = [0.1,1,10,100,1000]\n",
    "kernels = ['linear','poly','rbf']\n",
    "data=[[\"线性核\"],[\"多项式核\"],[\"高斯核\"]]\n",
    "for i in Call:\n",
    "    for j in kernels:\n",
    "        clf = svm.SVC(C=i, kernel=j) #linear 线性核函数 poly 多项式核函数，rbf高斯径向基核函数（默认）\n",
    "        clf.fit(train_X, train_Y.ravel())\n",
    "        te = clf.predict(test_X)-test_Y.ravel()\n",
    "        ta = clf.predict(train_X)-train_Y.ravel()\n",
    "        te*=te\n",
    "        ta*=ta\n",
    "        te = int(np.sum(te))//4\n",
    "        ta = int(np.sum(ta))//4\n",
    "        data[kernels.index(j)].append(\"%d(%d+%d)\"%(te+ta,ta,te))\n",
    "#         print(\"C=%f  核函数：%s %d(%d+%d)\"%(i,j,te+ta,ta,te))\n",
    "# 表格绘制\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "# print(data)\n",
    "fig, ax = plt.subplots()\n",
    "ax.axis('off')  # 隐藏坐标轴\n",
    "\n",
    "# 创建表格，设置文本居中\n",
    "table = ax.table(cellText=data, colLabels=['核函数', 'C=0.1', 'C=1', 'C=10', 'C=100', 'C=1000'], cellLoc='center', loc='center')\n",
    "\n",
    "# 设置表格样式\n",
    "table.auto_set_font_size(False)\n",
    "table.set_fontsize(14)\n",
    "table.scale(1.2, 1.2)  # 放大表格\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4660933a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-12\n",
    "# 错误随c的变化\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "from sklearn import svm\n",
    "# 基本参数确定\n",
    "epoch = 10000\n",
    "xuexi = 0.1\n",
    "# 加载数据\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "# 训练集和测试集长度确定\n",
    "split_rate = 0.7\n",
    "train_m = int(len(data)*split_rate)\n",
    "test_m = len(data) - train_m\n",
    "d = len(data[0])-1 # 输入特征维度（从0开始计数）\n",
    "# 标准化\n",
    "mean = np.mean(data[:train_m,0:d],0) # 平均值\n",
    "stds = np.std(data[:train_m,0:d],0,ddof=1)# 标准差\n",
    "data[:,0:d]  = (data[:,0:d]-mean)/stds\n",
    "data[:,d:] = data[:,d:]+(data[:,d:]-1) # 将标注0改为1\n",
    "# 制作训练集和测试集\n",
    "train_X,train_Y, test_X,test_Y= data[:train_m,0:d],data[:train_m,d:],data[train_m:,0:d], data[train_m:,d:]\n",
    "Call =list(np.random.uniform(0.1, 16, 150))\n",
    "Call.sort()\n",
    "kernels = ['linear','poly','rbf']\n",
    "tadata=[[],[],[]]\n",
    "tedata=[[],[],[]]\n",
    "for i in Call:\n",
    "    for j in kernels:\n",
    "        clf = svm.SVC(C=i, kernel=j) #linear 线性核函数 poly 多项式核函数，rbf高斯径向基核函数（默认）\n",
    "        clf.fit(train_X, train_Y.ravel())\n",
    "        te = clf.predict(test_X)-test_Y.ravel()\n",
    "        ta = clf.predict(train_X)-train_Y.ravel()\n",
    "        te*=te\n",
    "        ta*=ta\n",
    "        te = int(np.sum(te))//4\n",
    "        ta = int(np.sum(ta))//4\n",
    "        tadata[kernels.index(j)].append(ta)\n",
    "        tedata[kernels.index(j)].append(te)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "fig, axes = plt.subplots(2, 2)\n",
    "x =Call\n",
    "axes[0][0].plot(x,tadata[0],color='r',label='train',marker='x')\n",
    "axes[0][0].plot(x,tedata[0],color='b',label='test',marker='o')\n",
    "axes[0][0].legend()\n",
    "axes[0][0].set_title('线性核')\n",
    "axes[0][1].plot(x,tadata[1],color='r',label='train',marker='x')\n",
    "axes[0][1].plot(x,tedata[1],color='b',label='test',marker='o')\n",
    "axes[0][1].legend()\n",
    "axes[0][1].set_title('多项式核')\n",
    "axes[1][0].plot(x,tadata[2],color='r',label='train',marker='x')\n",
    "axes[1][0].plot(x,tedata[2],color='b',label='test',marker='o')\n",
    "axes[1][0].legend()\n",
    "axes[1][0].set_title('高斯核心')\n",
    "axes[1, 1].axis('off')# 隐蔽第四个\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e026d5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-13\n",
    "# k交叉验证 错误随c的变化\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "import random as rd\n",
    "from sklearn import svm\n",
    "# 基本参数确定\n",
    "epoch = 10000\n",
    "xuexi = 0.1\n",
    "k = 4 # 重交叉验证k的值\n",
    "# 加载数据\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "rng = np.random.default_rng(100)\n",
    "rng.shuffle(data)\n",
    "# 训练集和测试集长度确定\n",
    "split_rate = 0.7\n",
    "alllines  = len(data)\n",
    "train_m = int(alllines*((k-1)/k))\n",
    "test_m = alllines - train_m\n",
    "d = len(data[0])-1 # 输入特征维度（从0开始计数）\n",
    "# 标准化\n",
    "mean = np.mean(data[:train_m,0:d],0) # 平均值\n",
    "stds = np.std(data[:train_m,0:d],0,ddof=1)# 标准差\n",
    "data[:,0:d]  = (data[:,0:d]-mean)/stds\n",
    "data[:,d:] = data[:,d:]+(data[:,d:]-1) # 将标注0改为1\n",
    "# 制作训练集和测试集\n",
    "# train_X,train_Y, test_X,test_Y= data[:train_m,0:d],data[:train_m,d:],data[train_m:,0:d], data[train_m:,d:]\n",
    "Call =list(np.random.uniform(0.1, 10, 25))\n",
    "Call.sort()\n",
    "kernels = ['linear','poly','rbf']\n",
    "tadata=[[],[],[]]\n",
    "tedata=[[],[],[]]\n",
    "for i in Call:\n",
    "    for j in kernels:\n",
    "        tadata1=[]\n",
    "        tedata1=[]\n",
    "        id = kernels.index(j)\n",
    "        for t in range(k):\n",
    "            start = t*test_m\n",
    "            ends = (t+1)*test_m if ((t+1)*test_m ) <= alllines else alllines\n",
    "            if t == 0:\n",
    "                train_X,train_Y= data[ends:alllines,0:d],data[ends:alllines,d:]\n",
    "            elif t == 3:\n",
    "                train_X,train_Y= data[0:start,0:d],data[0:start,d:]\n",
    "            else:\n",
    "                 train_X,train_Y= np.concatenate((data[0:start,0:d],data[ends:alllines,0:d]),0),np.concatenate((data[0:start,d:],data[ends:alllines,d:]),0)\n",
    "            test_X,test_Y=data[start:ends,0:d], data[start:ends:,d:]\n",
    "            clf = svm.SVC(C=i, kernel=j) #linear 线性核函数 poly 多项式核函数，rbf高斯径向基核函数（默认）\n",
    "            clf.fit(train_X, train_Y.ravel())\n",
    "            te = clf.predict(test_X)-test_Y.ravel()\n",
    "            ta = clf.predict(train_X)-train_Y.ravel()\n",
    "            te*=te\n",
    "            ta*=ta\n",
    "            te = int(np.sum(te))//4\n",
    "            ta = int(np.sum(ta))//4\n",
    "            tadata1.append(ta)\n",
    "            tedata1.append(te)\n",
    "        tadata[id].append(sum(tadata1)/k)\n",
    "        tedata[id].append(sum(tedata1)/k)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "fig, axes = plt.subplots(2, 2)\n",
    "x =Call\n",
    "axes[0][0].plot(x,tadata[0],color='r',label='train',marker='x')\n",
    "axes[0][0].plot(x,tedata[0],color='b',label='test',marker='o')\n",
    "axes[0][0].legend()\n",
    "axes[0][0].set_title('线性核')\n",
    "axes[0][1].plot(x,tadata[1],color='r',label='train',marker='x')\n",
    "axes[0][1].plot(x,tedata[1],color='b',label='test',marker='o')\n",
    "axes[0][1].legend()\n",
    "axes[0][1].set_title('多项式核')\n",
    "axes[1][0].plot(x,tadata[2],color='r',label='train',marker='x')\n",
    "axes[1][0].plot(x,tedata[2],color='b',label='test',marker='o')\n",
    "axes[1][0].legend()\n",
    "axes[1][0].set_title('高斯核心')\n",
    "axes[1, 1].axis('off')# 隐蔽第四个\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5743b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-14\n",
    "# 经典求解 k近邻\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "# load dataset\n",
    "df = pandas.read_csv('wheelchair_dataset.csv')\n",
    "data = np.array(df)\n",
    "data=data.astype(np.float64)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(400)\n",
    "rng.shuffle(data)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] - 1\n",
    "# 数据最大最小归一化处理\n",
    "maxs = np.amax(data[:,0:d],0)\n",
    "mins = np.amin(data[:,0:d],0)\n",
    "data[:,0:d] = (data[:,0:d] - mins) / (maxs - mins)\n",
    "# 样本长度\n",
    "lens = data.shape[0]\n",
    "# 设置k交叉验证中 k的值\n",
    "k = 4\n",
    "# 测试集长度\n",
    "test_m = lens // k\n",
    "# k近邻分类 中的k值\n",
    "kk = [i for i in range(1,21)]\n",
    "Error = [[],[]]\n",
    "for i in kk:\n",
    "    ta = 0\n",
    "    te = 0\n",
    "    for j in range(k):\n",
    "        # 区分训练集和测试集\n",
    "        start = j*test_m\n",
    "        ends = (j+1)*test_m if (j != k-1) else lens\n",
    "        if j == 0:\n",
    "            train_x,train_y = data[ends:,0:d],data[ends:,d:]\n",
    "        elif j == k -1:\n",
    "            train_x,train_y = data[0:start,0:d],data[0:start,d:]\n",
    "        else:\n",
    "            train_x,train_y=np.concatenate((data[0:start,0:d],data[ends:,0:d]),0),np.concatenate((data[0:start,d:],data[ends:,d:]),0)\n",
    "        test_x,test_y = data[start:ends,0:d],data[start:ends,d:]\n",
    "        # 计算距离\n",
    "        ans = 0 # 测试集错误率 \n",
    "        for t in range(len(test_x)):\n",
    "            _ = np.sum((train_x-test_x[t])*(train_x-test_x[t]),1)# 得到全部的距离\n",
    "            __ = np.argsort(_) # 排序\n",
    "            for h in range(i, len(_)):\n",
    "                if _[__[h]] != _[__[h-1]]:\n",
    "                    break\n",
    "            _ = [train_y[hh] for hh in __[:h]]\n",
    "            if test_y[t]  != stats.mode(_,keepdims=False)[0][0]:\n",
    "                ans += 1\n",
    "        te += ans\n",
    "#         print(ans)\n",
    "         # 计算距离\n",
    "        ans = 0 # 测试集错误率 \n",
    "        for t in range(len(train_x)):\n",
    "            _ = np.sum((train_x-train_x[t])*(train_x-train_x[t]),1)# 得到全部的距离\n",
    "            __ = np.argsort(_)\n",
    "            for h in range(i, len(_)):\n",
    "                if _[__[h]] != _[__[h-1]]:\n",
    "                    break\n",
    "            _ = [train_y[hh] for hh in __[:h]]\n",
    "            if train_y[t]  != stats.mode(_,keepdims=False)[0][0]:\n",
    "                ans += 1\n",
    "        ta += ans\n",
    "    Error[0].append(ta/k)\n",
    "    Error[1].append(te/k)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "plt.plot(kk,Error[0],color='r',marker='o',label='train')\n",
    "plt.plot(kk,Error[1],color='b',marker='x',label='test')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45ed77d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O3MdV9W9qbn9gUVB39OhRuZOZHd6DDz4onnjiCf37M2fOiHbt2lW7zpRr164JlUqlf5w6dcqyTs/Xl50d1RqTf6CA6cdddxluGxBgetu+fQ23jYgwvp0V+vbtK5YsWWKw7IEHHhANGjQQly9fNliu1WpFbm6uKCwsFLt27RIPPfSQaNGihcE2pjo8ACIjI0MIIYRGoxEARHZ2tsl22Tuoc2QfZ3X/JgT7OKp1VQZ1lvRxPj416+Os5Ix9nC2COotuv7Zo0cKiq4BqtRrNmzfXvw8ODkZOTk6160x5/fXXERISon9ER0eb15DERMDXFygqAry85Hsistj999+PkJAQg2VeXl5YsGABIiMjMXnyZKhUKn2OvuqEhYXhxhtvBFCWz08IYdtGW8CRfZzV/RsAvPwy4O0t+zhfX/Zx5Dp8fBzdAgOu3sfZdfart7c3/Pz89O/9/f1RWFhY7TpTZsyYAZVKpX+cOnXKvIYkJ8vODgBKS4EHHrDsRIhs6coV04+1aw23PXcOeOUV+VqXxPiVV+S2339vuG12tvFjWsHLy8toxxMYGFhp2ZYtW7Bw4UIcPHgQe/fuxcSJE83+nODgYKva5yxs2cdZ3b8BwGuvASUl8nVRkekB6ES1wZI+7tln5XPF/s3cPs5K7trH2TWoCw8Px/nz5/Xv8/Pz9ZFqVetM8fPzQ3BwsMGjWrpBl0lJwJQpctmyZcCrr1p+QkS2EBho+uHvb7jtggXAnDny91ejkc9z5sjl18vKVHtcK7Rq1QqbN29Gbm4uNm/eXOW30ivXO1aVSoUdO3bg2WefdejVttpkyz7Oqv4NKOvjBg6U75s2NT4Anai2mNvHJSeb7t8CA83r46zkrn2cXYO62NhY7Nq1S/8+IyMDkZGR1a6zmfIBXWKifNYVyk1KYqdHzq3i7y9Q9nts5/+0ExMTceLECTRv3hxPPPEESktLTW47ePBgDBs2DF27dsWkSZPw6KOP4syZM/jvv//s1j5n4VR93JIlcnjJyZNAQgIDO3JuDuzf5Ee5aR9X5Yg7E1BhELFKpRJFRUWVtjt//rzw9/cXv/76qyguLhZ33323eOqpp6pdZ65qBw6++mrlAcNffSUHVyqVQjz9tEWfR2SJKgcSm8PY769OUpJc72HsPVFCxxn6OLPOqeLvyMCBsn+bNctjf0eodrB/s71an/2q36lCh9esWTOxbt06o9t+8MEHwsfHR0RERIhmzZqJs2fPmrXOHFZ15KWlQgwYIDu+wYPleyI7qHGnR5U4KqhzRB9n1Tl9+aXs21q2ZN9GdsX+zfZsEdQphLD/jeEjR47g4MGD6Nu3b6VxIlWtq45arUZISAhUKpVl+x4+DHTqBAQFAX/8IcegENnYtWvXcPz4cTRv3hz+FcfKkVVM/Zta3RfYiD36OKvOqaAAaNRIDiDfsQPo1cuS0yAyG/s326vq39Tc/sDb3o0E5IDEVq1aWbzObm64AVi9GujdG6hXr3Y/m4jcjtP0cYGBwNixwOnTrCZB5IFqJahzSsOG2eY4s2bJztNYXqjkZECrdbkadkTkwhYtkhMmiMjj8C9fCJk3p0IdN7Ox9iIRORMGdEQeyyOu1Gm1QHo6kJsrM5rExZWLtebNA156Sd6K3bbN8g5Rd4Vu5kz5QY8+Cnz2WeWp2kREtenUKWDXLmDMGEe3hIhqidt/pUtNBWJigP79gQkT5HNMjFwOALj3XjkOZccOYOlS6z4kMRF4/HFg9mwgKooBHRE51smTQLNmstMrlwCZiNybWwd1qanA6NFyzHB5OTlyeWoqgOjosjFv06YBFy9a9iGlpcD8+cDixWXLFIqy0k5ERLWtaVOgWzdZOuyrrxzdGiKqJW4b1Gm1siqYsYQtumUJCXI7TJkCdOwoA7oZM8z/kDNnZGmeF18sq7uoVMoPmDOnpqdARGS9++6Tz198UbPjzJplOrt/cjInghE5EbcN6tLTK1+hK08IOeQkPR2Ajw+wcKFc8cknwM6d1X9AXh7QpQvwyy9yf0Defi0pqbUyJ+QZtFpgyxZg5Ur5XEWJQpvp168fPv/8c5sdT6FQIDs722bHIzOMGwd4ewO//w4cOmT9cTgZjOzIEf0b4L59nNsGdbm5Fm53663AQw/J1088IW+rViUsDHjgAZnos7i4LJAD5Fi62bPLxtYRWanaMaFEpjRoAAweLF/X5GqdsXqcxup2ElmI/Zsd2KfYRe2oqmxGWpqsllPdIy2t3E7nzgnRo4cQP/1k/AP//luIcqWDhEYjxMsvV65fV1oqxPDh8gPGjq3ZSZLLqmkZnbVrhVAoKv/OKhTysXatjRsshHj88ccFAIPH448/LoQQYs+ePaJ79+4iODhYjBw5Uly+fFm/3xdffCGaNWsmAgICxODBg8WFCxeEEEK0adOm0vFWrlxpdftqq0yYM7DJOa1aJX9pmjYVQqutWYNmzZLH8vGRz6bqdpJHcMX+TQjn7uMcVvvVWVR1kiUlQkRFGf+l0f3iREfL7QwYKzJcWirERx8J4e0tRGSkEMXFRj8vLU2IFSvkc8n0l8s604ICW50yuRBTf6BXrph+6DbV/f6a+jKiUMj15X9/TR3TEoWFhSIvL0/07t1bfPDBByIvL0+/rF69emL27NnixIkTYtCgQWLixIlCCCHy8/OFt7e3WLlypcjOzhZDhgwR06dPF0IIoVarRV5engAg9u/fL/Ly8kRRUZHN/00Z1Jlw9aoQISFC1KkjRFZWzRo0bVrZL6Cvb82ORS6vqgCkuj7O3P4tP7/641rKmfs4WwR1bpunTqkE3nlHznJVKCpPmBACSEkxMhxEN34EAJ56Sm746KNl14P9/WVdxdBQ/S6pqXKuRfkxfFGRSXgn4jziTy4C5s7lxAnSq1vX9Lq77gI2bTJvTOjp03K7fv3kspgY4MIF49uaq06dOqhTpw68vb0REBCA0Ou/52vXroWPjw8SExOhUCgwdepU3Hd9IL5SqYSPjw80Gg0aNGiAb775BuL6hwYFBemPHRwcrD8e1RJ/f/kL1akTUJOauJcuAf/3f2Xvi4rkLVjeeiUjquvjpk0zr3+79VYgI6NsubE+ztLq9e7ex7ntmDoAiI8H1qwBIiMrr6tfHxg61MhO5cePNGoEtG1bFtANHgwcPlwpoDOaNuWMF0Zf+AipGAm8+abcj8hMFo8JtbOcnBycP38eYWFhCA0NxZgxY3D+/Hlcu3YNderUwerVq7Fo0SLUr18fgwcPxrFjx2qnYVS93r1rFtABwAcflM3wB+QkMU4GIyuZ228VFdm3HeW5Sx/n1kEdIAO77GwgLQ1YsQL4/ns5fvj8+SrGDicmAj16yN8oXeLOSZPkzuUqTlSbNkUBJPh/BG1RCfD005Z/pSC3dOWK6cfatXKbxo3NO1b57bKzjR/TGl5eXvpvogAQFRWFm2++GRkZGcjIyMD+/fvx559/wsfHBxcvXkRYWBh27NiB//77Dw0aNMDUqVMNjqdQKAyORw5SUGD5PuUnRWzfLpcdOSJzcTKwIyOq6+PM7d/KXxwGjPdx1nLXPs7tgzpA3lHt1w8YP15ebHvhBeCGG4CIiCp2+vHHsgDO17cs5Uk51d8iU+DUtQZI974N+OknYN26Gp0HuYfAQNMPf3+5TVycLE6iUBg/hkIh82bHxVV/XGu0atUKmzdvRm5uLjZv3ozBgwfjxIkT2LNnD5RKJb766isMHjwYQghcuHABt99+O3744Qeo1Wp4eXmhtMLs8VatWmHTpk3IycnBtm3brGsUWW/nTuCmm+RtBUssWiTvMuhmufbqBbRoIf83bdtWLq+tHBTkMqrr48zt3wYMqP641nLbPs6q0XxOwtqBxBqNkQkSFSUllQ0INjHTa8UK82bYrhjxtRCtWwvx668WtZNcm61mh1Wc7GPv2WFCCHHy5EnRu3dv4efnJ1q1aiWKior0M8MCAgJEbGys2L17t377Dz74QMTExAh/f39xyy23iMzMTIPj/fLLL6Jly5bC399fTJgwwep2caKElf79V/7yeHkJceaMefvs2VM203XXrrLlr74qRKNGQnz6ac3bRS7Llfs3IZyzj+PsV3t15LqAThfIVXx/ndlpU37UCHHtmm3bSE6vpp2eELJjqzhLLDra/h2es2JQVwM9e8pfoLfeMufDhWjRQm4/apTMAKBz5YrRDADkWdi/2R5nv9bQtWvAZ5/Jy7z6SRPGkmrqnssnF4a8hNykiawWZoxCIcfvxd3uCzDpOlkhPh4YPlze6s/NlWNR4uKYxJ+scP/98jbsF18Azz1nejsh5BjiY8eAZs1klZ3y98lqcs+LqBz2b7bn0UHd++/LqdXt2gF33319CJ1WazxLuu59ufEjSiXQoYPxoE6XRqWgQI4pbtMGcvbY++/LwcarV5seUEBUjm5MKFGN/O9/cmbX/v3AX38BnTsb3+7zz2XNJqVSPoeFGd9Oq5VB4q232q3J5P7Yv9mWR0yUMOXRR2V2koMHy2YdYtYs07mXEhMNileXlsovsoBMkVJeZCTQqpUcT3znncB//0EWm50+XX6Y/gOJiGpBeDgwZIh8bWrq/8GDMj8nIO9a9OxpfLuSEjnbLC5OBolE5BQ8OqgLCQESEuTr5OTqy71W5OUl70xkZclLx7q0KWlpcur1jh1yotjx4/L2bkGD5sCLL8qdp06t2XxsIiJLXU+miuXLjc9aXb0aKCyU0w51fZUx3t5A167ydU3qyhKRTXl0UAcAzzwj83IeOABs2GD+fuXT0bRrZ5g2pV8/+b5BA5narl494PffZcFi7bTpMpo8fdp4lYnkZIOrgQDke1O5oIxtT05FOEHuInfBf8sauusuObbuo4+Mr585E/jyS2DZMoOcnEbdf798Xr7cMDExeRT+TdqOLf4tPT6oCwuTgR0gh9KZ82+6caO8pVpVjjqdG24AvvkG8POTz1Om14G462658s035e0OHd0kjYqjRHWlyyoGdqa2J6egvP5zKarNtOhurrCwEADg4+Pj4JY4F60W2LJFDoHbsqWK1HG+vsDSpcCwYab7jXvuMS877ODBMtnn2bPAL79Y2XJyVbq/Qd3fJNWcLfo3j54ooZOQIOvAZmTIgM1o+bDrLl2SY/HOnpWVc15/vfrj9+olv8yOGSOH0iVmLEfDfXtlUs8hQ+RMijlzDGfdCgGoVMDFi8CgQXLb8rNvjc3SJaeiqy14/vx5+Pj4wKu6Kx9kkhAChYWFOHfuHEJDQ/UBM5moPR0la1/Hx1fYeNYsGcyV7zNOnpRjfZs2lZlhTVz512orzlL0gXL8eOC99+SVvUGDbH1q5MSUSiVCQ0Nx7tw5AEBAQAAUnPxnFVv2bwzqIG+PPvUUkJkp+7WqPPOMDOjati2LscwxapScVNa3L9CwIYDvv4e2dVukH4tGrs/9aKw9hbj/jYdyxQrZSV66VPnr9rhx8kPnzJElzBjQOTWFQoHGjRvj+PHjOHHihKOb4xZCQ0PRqFEjRzfDaehqT1e8w5CTI5evWVMhsNNd9b94EQgKAm68UX6j3bFDrk9KMvk5RgPHp59BPN6T1XLy8+UxyWPo/hZ1gR3VjC36N4Vw4RviarUaISEhUKlUCK5hwerS0uqHkKxbJztILy/gt9+AW26x/vNSU4FnHilATl5Zzqeo8EK8c+lexKNcObGAAHmLo149GcyNHCkDOm9v+cxvRk6vtLSUt2BtwMfHx+Q3WFv2Bc6iunPSaoGYGNPDQBQKGXgdP17hTqvuKj8A1KkDXL0qXyckVC62CdOBo+x6BNY0mYL4nPeAr74Cxo618CzJHWi1WhQXFzu6GS6tqv4NML+P45W666oL6M6fBx5/XL5+8cWaB3SjRgFAgMHynEt1MBprsebtE4gf6yMDOV0xUEB2xrrgoKQEmDhRZk8mp+bl5QX/8j9HIhuovva0zKKUnl4hD1hiIpCXJwM4XUA3ZozRgE6rlVfojH31F0JejU4oegPDd94H5S031+h8yHUplUoOiXASHORTwenT8hZrxXG/kyfLwK5jR+DVV60/vq6TBAQAw6ts4vr7hJQYaBtFVg7odGPonnxSLluyxPSsWCJya7m5NdhuwYKyq/xeXsCqVUb3NStwPF8H6ddiedeAyAnwSl0Fb78th7Tt2yf7urNnZVWcjAx5C2PpUjmT1VplnaTxDlBAUfnbdcVJESdPygR5xcWVSpcRkWcwZ4Kqye2Sk2VE5u0tr/onJxvtQywOHOXlO/N2IiKb45W6CqZNk/3cb78Bt90mc8sNHy7zcc6YUZZv01pWfbuuWLqsaVPggQfk69atq8hfQETuKi5OjpkzFUMpFLKudVxchRXlvyQWF8tnYymTYEHg6HsReOQRoEcP8/JCEZFdMKirYNcu43k0z5wBXntNjoerCau+XRsrXTZ9urx0+O+/ZaV/iMhjKJUybQlQObDTvU9JMTFJovyXxMREk4GdLnA0RR843u4ry+ns2SMzrRORQzCoK6dsvFtlui+fCQk1uzBm9bfrilq2lJcRARltEpHHiY+XaUsiIw2XBwUZSWcCVL7qr6ML7Cp0buUDR2OEkN2PMjRIzswHWDaMyIEY1JVjyWwya1X17Vqn0rdrU156SQ78Ky2Vt1GIyOPEx8ta02lpZXOoIiLKYiwDxq766yQmGk083KmT8c11fdSPP15foCsbtnJl2Sx9IqpVDOrKqdFsMguY+nYdHW3i27UpbdsCx47JorUsm0TksXS1p+fPlxO7jh2TQ0lsQaUCeveWVcHS0uRd1rQ0+VAqZbWcL78EcPvtQKNGMrHxDz/Y5sOJyCKc/VpOjWaTWSg+Xk7AMCy7Y0UZ12bNat4YInILgYHA+PGyII2tUiPefDOwfTug0VSe+T9zpkzx9OSTQK9e3mhxzz0yhcCyZbK+LBHVKlaUKEeXoT0nx/gELpMZ2m3o4EFg6lTgww+BFi0s2PHUKVnJ+7777NMwIifmiRUlTKnNrCIlJfIK4Y4dcuJr+vv74X1zF8DXV+aDCgurnYYQuTlz+wPefi3HqtlkNvbcc3KMyuTJFmQGOH0aaNUKeOgh4OhR+zWOiJyeLQO6LVuACxdMr/f2lrdeg4Pl7d4Ptt8or9BNn87UJkQOwKCuAlPj3aKiLBzvZqWUFPkl94cfgNWrzdwpKkqOZ9FqgXnz7Nk8InIR//4ry7Fa69o1OdmicWPgr79MbxcTA3z8MfDUU8Bjj0GO8Z09GwgPt/7DicgqvP1qglZrg/FuVpo1S/aJjRvL27EhIWbs9NtvcjSzjw9w5IhMUEzkIXj71dDhw0CbNjW7C7p2LTB6tPzOeOJE9fWxich+ePu1hnSzycaPl8+1Wat4+nRZKCI3F3jlFTN36tVLlsAoLgbeeMOu7SMi53bDDTIVSVGRBVf8K9Clm7vnHssCOq0WSF1VDLFuvcmaskRkHwzqnJC/P/DRR/L1Bx9YkKBdFwEuXixLYBCRx9LNmbImF/DFi8B33xkexxylpcDgG45i1FgffB6/HlsS1mPlCoEtW67nNU5ONpoLj4hsg0Gdk7rtNuDee+VY46oyuhvo10/egtVogLfesmfziMjJTZggJ01s3y5n7Fvi66/lRf+bbgI6dDB/Py8v4LYWJwAAE/EZ+ueuwIR7FOjfH4gJVyF15p/Gb3vMmmW09iwABoJEFmBQ58Tefls+liwxcweFQmaFDwyU09GIyGNFRsovh4BMEGwJ3dU9azIktX78NgACosJ/LznqIIzGWqR2MFLRQqk0WntWX6u2Nse/ELkwTpRwN0IAeXmceUYexR37Aluc09KlwIMPyjF2hw6Zl+7k7FlZ3aa0VObsbNTI/M/T5fo0VW7RINfneynA+fNyoUIhZ6Zt3Spn8i9eLBMYz5xpvFYtkYcxtz9gRQkXodEA69bJDrbKGbkKBQM6IgIgUzA98QTw33/AyZPmFaBp1EgOyd2xw7KADrCsfna/jz4C/vmn8ka//AI0by43ZkBHZBEGdS6gqEh+0z550nB5VJQcb2cyd156utzpnnvs3kYicj5BQbJG6403WlY2rH59YMQIyz/PovrZ998vLwsKUfY4cgT46Sf52teXAR2RhRjUuYCNGysHdIC8NTJ6tImkyPffLwfGhIYCQ4cajrFLTpb3STj4mMjt3XKL+dtqtTUbvmZR/ezxL1VekZwsgzpfX/ltNjmZgR2RBThRwslptcCUKcbX6UZDJiRcTxdQXsuW8vnyZZkXRYcDj4k8khBAfn7V2zz9NNCnjywPZo24OCAqWAUFSo2uV6AU0cEqxMUZWanrm5KS5HiTpCTjkyeIyCQGdU7OkjEqBl59FRg1Sr6eMwcoKDDsNPntl8hj/Pwz0LYt8MgjprfRaGRZsfR0oKTEus9RKoF3Bv8AQGGifrYCKYN/qPydsmLfVFwM9OwplzGwIzIbb786OYvGqFT01VdAgwZyNmxIiLycx4COyOPUqydLh508CahUxksPbtoku4omTYD+/a3/rPivx2LNWHmHofwX0qAgYOFCBeInjK28U8W+qVs34MABOfYkKcnIrQgiMoZX6pycRWNUKvL2BlJS5GutVtaFZUBH5HFuuglo3x64dk3WdDWmfFmwmo7OiI8HsrPlJI3ly2WgqFaXDRmpZNYsw76pTx/5vH69XM7xv0RmYVDn5OLi5CxXU/mlFAqZU8roGBVA9qw6xcXyVgYReRSFQlaoAYyXDbt4UV6pA6xLOGyMrn72hAnAo4/KZV9+aebOuqm333zDq3REFmBQ5+SUyrIyYcbHqMiLcUa/WScny7F1L78sy4fde69cxvEpRB5Hl9loy5bKs+lXrZLf+W68EejUyfafrQsof/pJ5syrVt++cub+uXPArl22bxCRm2JQ5wLi42XakshIw+VRUSbSmQCGA4/nzJGjn7/4gjPKiDxU06byyhkArFhhuK4mZcHM0aoV0KOHrFKxcqUZO/j4AEOGyNfr19unUURuiEGdiyg/RmXFCvmcng507Ghih4oDj3WX9RITgalTZZJPIvIo5W/B6sa3CQE89RQweLC8VWrvz7b4Fuy6dVUMxiOi8lj71UUtXAhMnlx2Fc9sf/0lb20AwL59QIsWdmkfUW1yx77AHuekUsm8lvfcI0usmlML1lbOn5cTJkpKgKwsoF27ana4cgWIiJC5Vv7+W870IPJQ5vYHvFLnonr1kl9ev/1WpiEwW9u28nH5sixHcfWqvZpIRE4mJARYsgQYMKB2AzpAlh4bPBgIC5PpVapVty7w6afAn3+aEQESEcCgzmV17ixvvRYVAatXW7Cjr6/cISJCdpZPP223NhKRc9Jq5YSJuXOBxx4DTpyonc/96COZU3P4cDN3uPdeoEuX2o9AiVwUgzoXpVCUDWo2e4yKTlSUTEzs5QUsXiwfROQRUlPlpKv+/eXE+E8+kXc2U1Pt/9mRkYCfn/0/h8hTMahzYRMmyOAuPd0wHZ1Zbr9dTqQA5OC8P/+0dfOIyMmkpspRFxXTihQWyuW1EdgBcujI8eNmbrxtG3D//fK+MRFVyaKgLjMzE7GxsQgLC8O0adNQ3RwLIQTmz5+P1q1bIyIiApMnT0ZBQYF+/dChQ6FQKPSPAQMGWHcWHioqCrjtNvna4qt1ADBjBnD33XIg8ty5Nm0bETkXrVaW7qqq205IsH+u3xMnZIqTrl1l11Ot33+X03Wt6uSIPIvZQZ1Go8HQoUPRrVs37N27F1lZWfj888+r3Gfx4sV49913sXz5cuzYsQN79uzBpEmT9Ov37duHAwcOIC8vD3l5ediwYYPVJ+KpdGkCTJX+qZKXl+wsX3rJeJp5InIb6emGtVgrEgI4dUpuZ0/R0XIs8OXLwHffmbGDLrXJ1q3ApUt2bBmR6zM7qPv++++hUqmwYMECtGzZEnPnzsXiasZiLVu2DNOmTUP37t3Rpk0bzJ49Wx+4nT59GkIIdOzYEaGhoQgNDUVgYGDNzsYDjRoFfPaZ7O+sEhYGvPYaMG+e6YTEycmsvUjk4nJzbbudtby8yvLhmXXxrWVLWeZCqy2rZUZERpkd1O3fvx89evRAQEAAAKBz587Iysqqcp8LFy6gadOm+vdKpRLK6/Ws9uzZA61Wi6ioKAQGBmLcuHHIsyg3BwFAUBDw0ENAjdNYKZWy0kS/fsAvv5Qt11WmqGmFbyIXYOshJm+++SYaNmyI4OBgjBo1ChcvXrT3KZjUuLFtt6sJ3R2GjRvNTMmku1rH6hJEVTI7qFOr1WjevLn+vUKhgFKprDIQ69KlC9aX+yNcsmQJBg4cCAA4fPgwunXrhh9//BF79+5FdnY2XnrppSrboNFooFarDR5kyOpU0omJMonU1q3AsGHyPk35UmO6yhREbsrWQ0y2bduGpUuXYtu2bfjjjz9w7do1PPfcc7VwJsbFxclxuKaygygU8tZoXJz929Kpk0zLZHZKJl1Q98MPzK1JVBVhphdeeEFMnTrVYFlUVJQ4ffq0yX2OHz8u2rZtK2699VbRuXNnAUBs27bN6LZbtmwRERERVbbh1VdfFQAqPVQqlbmn4bYWLxaiSxchvv22BgcpLBSiUSMhACEUCvmclGSzNhLZi0qlqnFfsG7dOhEWFiYKCgqEEEJkZGSI3r17V7lPXFycSElJ0b/ftGmTCAoKEkII8eabb4pp06bp133xxReiZ8+eZrfHFudU0dq18k9b9+ete+iWrV1rs4+q1vz58rPj4szYuLRUiKZN5Q4bNti9bUTOxtz+wOwrdeHh4Th//rzBsvz8fPj6+prcJyYmBllZWVi0aBGaNm2KO+64A3EmvgaGhobiwoUL0FQxHWrGjBlQqVT6x6lTp8xtvts7cADIyKjhBLE6dYAdO+RrIWRRbV6hIw9h6yEmHTt2RGpqKo4ePYpz585h8eLFuOOOO0weqzbuROjKCkZGGi6PipLL4+Nt/pEmjR9vQUomhUJerevcGSgtrYXWEbkms4O62NhY7Nq1S/8+OzsbGo0G4eHhVe6nUCgQHByMzZs3Y968efrlo0ePNjje77//jkaNGsGvisyUfn5+CA4ONniQpBujsmGDrO9oteXLy14XF5uePEHkZmw9xGTw4MFo3bo1WrVqhYYNG6KgoADTp083eazXX38dISEh+kd0dHTNT8qI+HgZRKWlAStWyOfjx2s3oANkIPnGG/Lzy8XFpr39NrB/f9mtWCKqxOygrk+fPlCpVFi2bBkAYN68eRgwYACUSiXUajWKi4tN7jtnzhyMGTMGXbt21S/r3Lkzpk6dit27d2Pjxo1ITEzEk08+WYNT8Wxdu8ryiNeuWZneBCgbQ6frNJs0ke8Z2JEH8Pb2rvSl0t/fH4WFhSb3mTt3Lvbs2YO4uDjceOON+Prrr/HUU08BAFatWoUTJ07g0KFDuHjxIjp27Ih7dd++jKjNOxFKpZwTNX68fHbUPKhp0+Tne5nzP5G3t72bQ+T6LLmnu27dOlGnTh3RoEEDUa9ePZGZmSmEEKJZs2Zi3bp1Rvf5999/RXBwsDh58qTB8qKiIvHwww+LoKAg0bJlSzF79mxRXFxsSXPsMubElc2dK4ec9Otnxc5JSWVj6M6eLRtsM20ax9aR07NFXzBv3jxx7733GiwLCQkR586dq3K/0tJSkZWVJYYMGSLuuOMO/fIRI0aId999t1Ib8/LyzGoP+zcTrlwR4q+/HN0Kolplbn9g0VefESNG4N9//8XevXvRq1cv1K9fH4C8FWtKq1atoDJyP9DHxweLFy+uNtcdme+ee2Qe4S1bgJMnzbyloaPVGs5yHTECaNhQlhALCrJ/mnkiB4uNjcWnn36qf2/NEJMdujGpAEpKSvBfuXpcudcTwGn5t2Tgn3+ADz8EIiLMGMK7c6cso9OoEXDsmOmpvEQeSiGE1UkwHE6tViMkJAQqlYrj667r318GdXPnyipgRJ7AFn1BSUkJmjRpgrfeegv3338/Jk2ahJycHHz77bdQq9WoU6cOfHx8jO77xBNPoKCgQD88BZBDVBYsWICkpCTUqVMHKSkpqFOnDn777bdaOydXsGkTMGSI/A55+nQ1d1kLCmT0d+2anBl244211UwihzK3P+AgBTczcSJQrx7Qo4ejW0LkWry9vbFo0SJMmDAB06ZNg1arxdbrpVo6d+6MlJQUjDAySP/IkSNYsWIFMjMzDZYnJCTgzJkzSE5OxoULF9CzZ0/emTBi4EAZp/33H7B5s0yXaVJgIDBokJwRtn49gzqiCniljkzTaoHdu4HwcKBtW0e3hsgkW/YFOTk5lYaYOIIn9W9PPw28/74cQlJtWqbPP5dldLp0Af78sxZaR+R45vYHZs9+JQ/0/PNA797Ae+85uiVEtSYyMhLDhw93aEDnaXSTglNTge+/B1aulMNIjA4/HDJETpfNyDAjwR2RZ2FQ56YOHwbmzAFKSmpwkAED5PPGjTWoP0ZEVLXu3WXN2atXgbvuAiZMkOODY2JkoGcgIqKsltmGDbXdVCKnxqDODZWUyAtsiYnAzz/X4EC33SarTJw8CVQYL0REZCvr1gHXJwcbyMkBRo82EtiNHFm2IxHpMahzQ97ewLhx8rWlZcO0WnnbY+VKYMvuOtDedr2s0bff2rSNRESA7HOmTDG+TneDICGhwq3Y+HjgrbeAciloiIhBndu67z75nJoKfPddNWNUULZtTIy87aG//bFjOVIxUt6CJSKysfR0mcrEFCGAU6fkdnrR0cBzzwGtWtm9fUSuhEGdm4qNlWNUrl0D7r67mjEqkMtGj67cueaoAjEaa5C6szFw/nyttJ2IPIex26412Y7IkzGoc1OWjFHR3f4wNhdCCJmxPQH/B+33P9mptUTkqRo3tmK7WbOA2bOBpUuBMWNkUmKd5GS5nsgDMfmwGzJnjMpjjwFnzwIXLshUT1Xe/oAXTqEp0iPHoZ/NW0tEniwuDoiKkl84jX2xVCjket2EVwCAUgnMnAmEhgKXL8tbESNHyoBu5kxZ8pDIAzGoc0PVjVEBgIsXZVlXS+SeU1rfKCIiI5RK4J135B0EhcIwsNOVdk1Jkdvp6YrEzpwpn9evlzP0dQFdtUVkidwTb7+6IXPHntx8s7xip0v8WR1zb5MQEVkiPh5YswaIjDRcHhUll8fHG9kpMRF4+GH5etkyBnREYFDnlswNvt58E/j4Y1l1Jyqq7FtxRQoFEN2wCHHLJwGvvWazdhIR6cTHywIRjzwi3992G3D8uImATmfRorLX3t4M6MjjMahzQ7oxKlUGadFlY1R0tz9064xJeeBPKD/9GPjsM1aXICK7UCrL8gr/91+FW67GzJ1b9rqkRI6pI/JgDOrcUFVBmqkxKqZufwDAU08B8YkdAF9f4Ngx4J9/7NJuIqLOneXzoUMyJZNJukkRzz8vr9IB8j0DO/JgDOrclDVjVHS3P9LSgBUrgEcflct/+AHQ1qkrE90BrC5BRHYTGQmEh8tZ/FlZJjYqP8v1zTeBsWPlrYfHHmNgRx6Ns1/dWHw8MHy4nA2bmyvH2sXFVX1LQ6kE+vWTr4cOlfns/v0X+PprYMKQIcCPP8rqEtOm1co5EJFnUSiAZ58FfHyABg1MbKTVGk6KWLwY8POTr6Oiqi6dQ+TGFEK47gAptVqNkJAQqFQqBAcHO7o5bmnuXODll4G2bYHMjdlQtmouI79z5+TXaSIn4I59gTueExFZx9z+gLdfqUpPPQXceqtM0K5oHgN07Ci/Bf/wg6ObRkRk6Px5eTtWo3F0S4gcgrdfqUrBwRUKaQ8ZAhQVcQYsEdlNaakc9pGZKYeRmJqVb0AIoHdvuWODBsADD9i9nUTOhlfqyDJJSXL26z33OLolROSmioqADh1klYmcHDN3UijKkhG/8w6/eJJHYlBHZrl6VfaTdw7zQWmpo1tDRO7M3x9o00a+/usvC3Z89FGgTh1Z0Hr7dru0jciZMagjs1y9Kiea/fAD8M03kF+ljxxxdLOIyE3deKN83r/fgp3q1Sure6hL1knkQRjUkVnCw4Gnn5avk168AhFeT+Y8ISKyA6uCOgB45hn5vG4dcOKETdtE5OwY1JHZpk4FAgOBPw/Xxaart8mU77xaR0R2YHVQ17EjcPvtcrbF++/bvF1EzoxBHZktIgKYPFm+nh34BgQgExETEdmYrlzY4cNy+IdFpkyRZQ2Li23eLiJnxqCOLPLcc0BAALA3vy1+wGDLgrpZs0yX70lOluuJiCAr4EREyAtuf/9t4c533w2cPCmLXBN5EAZ1ZJEGDYAnnpCvkzAT2LoVUKvN21mpNF6XUVfHsar6ZUTkURQKmUd47VqgZUsLd/byAho2tEu7iJwZkw+TxZ5/Hjh1Cpi+ez5wogT46SeZUKo6ujqNM2eWvS9fmFu3nogIwIMP2uAgf/0lc9bpBukRuTFeqSOLNWoEfP01cNPo61+fv/3W/J0nTwb69y+7MseAjojs5b33ZDA3Y4ajW0JUKxjUkfXGj4c2eS5+7TsLK1cCW7bIsrBG5eYC06YBzZoBaWlyWWkp4OPDgI6IjCoqAjZtAubPt7JAxF13yfu4338vZ+sTuTkGdWS1xRndEDJvBm6f2BwTJsgLcDExQGpqhQ0vXQJatQLeegu4ckVe6tMpLjY9eYKIPJoQwIgRwIsvyiEfFmvZUtarBuRVOyI3x6COrJI69ms8+ohAQYHh8pwcYPQogdRhn5ctDA+XiYp79QLuvRfas+ewZUQKVo5JxZbxH0M7cxYDOyKqxM8PaNdOvrY4X51OQoJ8XroUuHzZBq0icl4M6shiWi0w5YfBEKh8P0TeIhFI+PY2aP8pl5j4s8+AQYOQ+mUBYoIvof/6KZiweiT6r3wMMcGXkDrzTwZ2RFSJLl+dRTVgy+vfXyYkLigAFi+2WbuInBGDOrJYejpwWh0CU78+Al44haZIX5gJ4Hr+z4AApP7dBqOx9vq+ZXLyQzAaa5GaeYOdW05ErsbqyhI6CoVMRgzIChMmB/4SuT4GdWSx3Fwzt7tlBACgSxcgLAwYmzoWAopK2+kGQCfsHMv+logM1DioA4B77gHq1QOuXWNpQ3JrDOrIYo0bW7bdmTNyKEtJieltBRQ4dUpeBSQi0tEFdf/+CxQWWnmQOnWAzZuBEyeANm1s1jYiZ8OgjiwWFwdERcm7GsYoFEB0tNwOAI4flykJzGHuVUAi8gwNG8qHEEBmZg0OtH498MYbxtexTCG5CQZ1ZDGlEnjnHQBCQIFSg3UKlAJCICWlrOpXaCgQG2vesc29CkhEnuPLL+Xt15tuqsFBdMnOZ88G/vijbDnLFJIbYVBHVon/OxlrMAqRwfkGy6OC87EGoxD/t+FM1mqv7qEU0TiJuKYn7NVkInJRAwbIWbA+PjU4SGKiTHg3axbQvTtw7hzLFJLbYVBH1tFqEZ90E7IvhSAtDVixQhaKOH4pBPFJN1WaYaa/uofKgZ18r0AKEqD8+MNaaT4ReaB584DISNk/NWnCgI7cjkIIq4qvOAW1Wo2QkBCoVCoEBwc7ujlkhtRUmV3g9OmyZQ0bAh8+9Dvi53WX02RPnwYCAhzXSHI57tgXuOM5WauwEPjkE+DgQWDhQtNX/M2ycCHw5JPyta8voNHYpI1E9mRuf8ArdVSr4uOB7Gx5Va93b7nsoYeA+DldgVGjZB6pGt1jISJ34+0tS0d//LGcwFojBw+WvS4qYtJzcisM6qjWKZVAv37AI4/I92lp1xeuWQNMmMCgjogM+PoC7dvL1zXKV5ecLGvA6u4EPPKIvAXLwI7cBIM6cpjbb5fPv/8OqFSObQsRObcaJyEuPyli8GC5rGVL+Z6BHbkJBnXkMNHRQOvWQGkpsHXr9YUqFfD228DTTzu0bUTkXHRBndU1YLXaskkRffrIZdu2yfdJSSwfRm7B29ENIM92++0yU/wvvwDDhgHIyQGefx7w8gKefRZo3tzRTSQiJ9C5s3y2+kpd+eTCd94J5OWV3S7g7FdyE7xSRw6l61N/+eX6gvbtgTvukJfvPmR6EyKSdFfqjh4Frlyp4cFuuEEGebqyN0RugkEdOVT//vL5yBH5xRkA8Mwz8vnTT4GCAoe0i4icS/36suKMEMA//zi6NUTOiUEdOVS9esCOHcClSzJFHQDgrrvkAObLl4Hlyx3ZPCJyIr/8AuTnA9262eBgarWsB/vZZzY4GJFzYFBHDterV4Vcw15ewFNPydfvviu/mhORx2vXDqhb10YHy8oCRo4EXniBfQy5DQZ15JweeggIDAT+/vt6IjsiIhvq2lV+m7x40TAhMZELY1BHTmHGDOCmm4Bjx64vCAmRiUHHjQMaNHBo24jIOajV8iL+wIFyLlWN+PoCPXvK1/qcSkSujUEdOYXt24GMjHKzYAHg//4PWLkS6NjRUc0iIicSECDnT/38M3D8uA0OWD5fHZEbYFBHTqFSahOghlW7icjdeHsDHTrI11YnIS6vfFDHcXXkBhjUkVPQBXW//lrutsqsWbJ0z6FDwNSphsmpkpMNk4miwj7GmNqHiFxGjZMQl3fLLbLW9Jkz5cZ+ELkuBnXkFG65Rd5aOX8eyMy8vlCplDUZe/cGUlKAL7+Uy3U1HJXKygfS7VMxsKtqHyJyGTWuAVtenTpA9+7y9c6dNjggkWOxTBg5BV9feSfkhx/kLdjOnVFWumfmTPn87rvAiRPAvHlyZsW0aWUHKCmRiYqfeQbQaKCdOQvph5sg966JaPzzMsQtmQWlru4jEbmsGteArejdd4HQUJYkJLfAoI6cxu23lwV1U6deX5iYCFy7BsydK9MO6FIPvP46EB4u68QCwL59QI8eAIBUjMQUZOP0l9HAlwBwP6KCh+OdDiGIr+2TIiKb0gV1x47J2bDBwTU8YNeuNW4TkbPg7VdyGrffLssANW1aYcVrr5l92zQVIzEaa3AakQbLc/JDMHo0kJpqo8YSkUOEhwNRUbKvOH3a0a0hci4WBXWZmZmIjY1FWFgYpk2bBlHNbCEhBObPn4/WrVsjIiICkydPRkG5Wp5bt25Fu3btEBERgQULFlh3BuQ2unQBcnKADz+ssCI5GdBq5T1aQE52KCkBnn22bJvYWGgLNZgSuRYCClT81db9qiYkyEMRkevKypJzG9q3t9EBv/4aiI+XZcOIXJjZQZ1Go8HQoUPRrVs37N27F1lZWfj888+r3Gfx4sV49913sXz5cuzYsQN79uzBpEmTAADnz5/HsGHDMH78eOzcuRPLly9HGisHeDSFwkgWE90Eh6QkQKORz7NmyduxXuV+fb28kL7bF6dzFACMp0IRAjh1CkhPt9cZEFFtCAqy8QH37AHWrZPjP4hcmNlB3ffffw+VSoUFCxagZcuWmDt3LhYvXlzlPsuWLcO0adPQvXt3tGnTBrNnz8aGDRsAAMuXL0fjxo2RmJiI1q1bY+bMmdUejzxDaSlw9CgMAzrdBIfERPneyAzX3Fzzjm/udkTkIZiEmNyE2UHd/v370aNHDwRcr7zeuXNnZGVlVbnPhQsX0LTcACmlUgnl9bFR+/fvx2233QbF9Usz3bt3xx9//FHl8TQaDdRqtcGD3Mu5c0DDhvK2SuE1L8OATkcX2FW4j9q4sXmfYe52ROScLl4E7rwTaN3aRsMpbr1VPh88KDshIhdldlCnVqvRvNyUb4VCAaVSiby8PJP7dOnSBevLjVFYsmQJBg4caPR4wcHByMnJqbINr7/+OkJCQvSP6Ohoc5tPLqJ+fZk6qqgI2N73ZdMpSBITKyUSjouTA6hNUSiA6Gi5HRG5rpAQYMsW4MgRG+UMrlevrBzh9u02OCCRY5gd1Hl7e8PPz89gmb+/PwoLC03uM3fuXOzZswdxcXG48cYb8fXXX+Opp54yerzqjgUAM2bMgEql0j9OnTplbvPJRSgUJkqGmUGpBN55x/jYPAXkTImUFOYfJtNsPRlMZ9y4cXj66aft1WyP4+1dFoPZJAkxwFuw5BbMDurCw8Nx/vx5g2X5+fnw1c1INCImJgZZWVlYtGgRmjZtijvuuANx1y+TVDxedccCAD8/PwQHBxs8yP3cdpt8tjSoA+QEtjVrgEjDjCaIqnsZa9bI9UTG2HoymM6PP/6IX3/9FcmmyteRVWxaLgxgUEduweygLjY2Frt27dK/z87OhkajQXh4eJX7KRQKBAcHY/PmzZg3b57J42VkZCCy4v/E5JF0V+r++AO4dMn8/V5/HZg9Gxg0CMjOBtLSgBWTdyAN/XC87Z0M6KhKtp4MBgBXr17Fk08+iXnz5iE0NNTOZ+BZbF5ZIi5O1oGtW1emTCJyQWYHdX369IFKpcKyZcsAAPPmzcOAAQOgVCqhVqtRXFxsct85c+ZgzJgx6Fouc/ewYcOwfft2pKWloaSkBG+99RYGDRpUg1Mhd9GkCdC2rUxBsmWLefucOlWW7WTzZnmLtV8/YPy0KPTDVigz9gHV3N4nz2bryWAAkJycjKtXr8Lb2xu//vprlbdzORHMMjatAQvIjketllfqvFlsiVyTRWPqFi1ahEmTJqFhw4ZYs2aN/spb586dsWnTJqP7HTlyBCtWrMBrr71msDwiIgJvv/02Bg0ahMaNGyMzMxOvvPJKDU6F3Inuat2vv5q3/UsvyWpiffoAw4aVLS9p0hT7GwzAnyUdZS4qIhNsPRns5MmTWLBgAVq1aoWTJ09i2rRpiI+PNxnYcSKYZXS3X0+cAC5fttFB/f1tdCAix1CI6kYCV5CTk4O9e/eiV69eqF+/fo0bcOTIERw8eBB9+/a1eIycWq1GSEgIVCoVx9e5ma1bgR9/BIYOBXr2rHrb338HuneXr/fuBbp1K1uXkiLryA7DBmyYkwm8/LLd2kyOY4u+4MUXX0RxcbFBdZvo6Gjs2rXL5NCQ7Oxs3HnnnYiIiIBarcZff/2Fbdu2IS4uDklJSVi8eDEOHz4MPz8/5Ofno1mzZvjqq6/0gV95Go0GGo3G4Jyio6PZv1WhUyc5Kerhh2VFmrg4G02EKiwErl+xJXIG5vZxFl9jjoyMtOnYt1atWqFVq1Y2Ox65h7595aM6QpRVC7v/fsOADgBuuUU+70IPiO0fm6g1QSQnb2VmZhosM3cy2KFDh/DCCy+gYcOG+slgp0+fxu23366f5R8UFITWrVvj+PHjRo/l5+dXKcMAmZaaKq/QnT4tv7gBMqXRO+/UYEKUWi3HbWRlyWR4gYE2ai1R7bCo9iuRs1m3TqaVqlMHqHCHHwBw002Aj4/AOTRE9oOzar195DpsPRksOjoaV69e1b8vLS3F6dOn0axZM9s33sOkpgKjR8uArrycHLk8NdXKAwcFARcuyJKE5X4XiFwFgzpyWgUFwKZNwMqVxtcLUZabeNo044mH/f2Bm26S1+d2arvbqaXkDmw9Gex///sfvv32W6xduxanT5/GjBkzoNFo0Lt3b7ufizvTaoEpU+Tff0W6ZQkJVlaaUCiY2oRcGoM6clq//QYMGQK88ILxDlyhAL79Fnj0URnUmaIbk8cv3lQVW08Ga9OmDb7++mvMmTMHrVu3xqZNm7BhwwYE2bwavWdJT698ha48IeRs+PR0Kz+AQR25MIsnSjgTTpRwb4WFQFiYLBn2zz/ADTdYd5yvvgLGjwdiW13CnqQf5RtyK7bsC2w9Gcxa7N+MW7kSmDCh+u1WrLDyT/2ff2ROJX9/OWiP4xzJCZjbH/BKHTmtgACgVy/5umJ1iaq+qVfUo4d8/vNIEK6+xKz+VLXIyEgMHz7coQEdmda4sW23q+SGG4AGDWSOpL17rTwIkWMwqCOnZqwO7KFDQIsWwH33yat41WnWDHhj9lV8h7vhnf0vcPasfRpLRHYXFyfHz1as76yjUADR0XI7q3BcHbkwBnXk1HRBXVoaUFoqX0+bBhQXAyoVUE25YACyj35hZh3c0eksfFAC7NhhvwYTkV0plTJtCWA8sBNC5qesUb66YcPkPV5d2QoiF8GgjpzazTfLVFGXLgHz5gFvvw1s3Cir+Lz5poUH0806/O03m7eTiGpPfDywZg1gKmVqvXo1/ID77gOWLwfuuquGByKqXSxwR07t22/LUhOULwZxxx1AmzbmH+faNeB7v3E4gPqYueMn2zaSiGpdfDwwfLic5ZqbK8fQLV0KHDkih1wQeSLOfiWnpUswaiqdyZo15meOLygAgoMFSksVOOXdHFHqLJmxmNyCO/YF7nhO9nb1qhySYZNSYaWlwMGDQEkJb8OSw3H2K7m0qhKM6liSYDQwsKwA+O6SrsC+fTVuIxE5lzp1DAO6c+dqcLB33gE6dgRefbXG7SKqLQzqyCnZI8Fojx7XK0tMXFw2vo6I3E5RkawH27o1cOyYlQfR5VNKTy+bpUXk5BjUkVPKzbXtdkBZvrpdh0JN50MgIpenVMoUc2o1cO+98g6qxbp2lckyL10CsrJs3kYie2BQR07JHglGdeXC9u0zL78dEbkmpRL44gsgOBjYuROYO9eKg/j4lF2tY746chEM6sgp2SPBaOvWsuzYtWvAX4NfAA4ftk1jicjpxMQACxfK10lJMrizGJMQk4thUEdOqaoEo7r3liYYVSjKbsHuTVOzoyZycxMmyIdWC9xzj7wda5HyQZ3rJoogD8KgjpyWqQSjUVGWpTMp7403gCNPvI3H8TErSxB5gA8+kHnrjh8HnnlGBnhbtgArV8pnkzPoZ80Cfv1V5kjJzQWOHi1bl5ws1xM5GQZ15NTi44HsbFkmbMUK+Xz8uHUBHQB06gS0HNIOCoBBHZEHCA2V4+vCw+Xwi5gYoH9/eQWvf3/5PjXVyI5KpbxvO2gQ8MMPZd8uk5OBmTNtlAyPyLZYUYKcnlIJ9OtnwwPqZkz8+69MZNWggQ0PTkTOJi4OePddWf2r4l3UnByZ5LzS1f/ERPk8cyYQGyuDO11Al5RUtp7IifBKHXmcZd+GYXTwj/gFt7EOLJEH0GqB6dOND4vTLTOazDwxUQZwM2fK27AM6MjJMagjj7N1K7BWPRC/4jbegiXyADVKZp6YKAO64mL5fssWTrIip8WgjjyOPgmxoqcV0+GIyNXUKJl5crJMbOl1/b/LX38F+vYFbrtNfkMkciIM6sjj6IK6PYH9of3wY8c2hojszupk5uXH0Gm1wLPPyuVeXnLWVr9+crbFzp1yNmxysvEDc7Ys1RIGdeRx2rcHgoKAK1cU+PtvR7eGiOzNqmTmxiZFvP22fF9aKidP+PjI27GHDskZXTNnVg7sOFuWahFnv5LHUSqB7t2BX34Bdu0COncsLbu1QkRuR5fMfPRoGcBVnDAhhJFk5lqt8UkRuvdaLbB2LfDRR7LArI+PXD5zpsy7tHgxMGcOJ1dQrVII4bppstVqNUJCQqBSqRAcHOzo5pALeeUV4LXXgIfCN+Czfstk52whrVYOrM7Nlbdt4uL4ZdxR3LEvcMdzcrTUVGDKlMqTJurUAbKyZM66GikqAho2BC5fLoseGdCRDZjbH/DyBHmkHj0AH+9SXLtUYFUJoNRUC5KYEpFTqJjMfPNm2Rf8739AvXo2+IBr12Q9MkD2KV5eDOioVvH2K3mkgQMB9cUS+Dd4GLigAQ4fBtq0MWvf1FR5G8fsJKZE5DQqJjPv2RMICLDRwYOD5ZU6ndJSYMYM4PXXbfQBRFXjlTrySL6+gH+wrxzsDJidr06rlbdvLE5iSkROqXxAJ4T8cmY13aSI2bOBrl3lsnnzTM+KJbIxBnXk2Xr3RikUZleWqFESUyJyWhcvAkOHyit3eXlWHKD8bNmZM4GpU+XyunWNz4olsgMGdeSxtm4Fuq6egRFYb/aVuholMSUip+XrKzOTnDoFTJpk8TDbyrNl//c/OYPqyhU5zo6X76kWcEwdeay6dYE/j4UgG7dCHDoExcWL1Y6WtjqJKRE5taAgOXmid29g1Spg8GDgoYcsOEDF5MK+vjLdSZMmwM0327KpRCbxSh15rM6dAX9/IA/hODzkOfmNuhpxcXIstClGk5gSkUvo3r3sLunTT8v5UzUybBgDOqpVDOrIY/n4lPW3u0a/BTRrVu0+SiUwofXvACrfm1EoAAiBlJ5fM18dkYuaNk2mKCookOmKrl6VRSNWrpTPVt9FNeNLI1FNMagjj6arA7trl/n7LBz+Az7GY4gKVhksjwpSYQ1GIb5jTb/eE5GjKJXAF18A4eHAvn0yQ0mN8lGWlspBeg0bAkeO2KvZRAAY1JGHKwvqBPD33zIjvBFaLaDSxXCJiXgsqSmy1eFIG/EOViwrQdpDy7BN3RkjZ9/EZKNELi4yEnjkEfk6P99wnS4fpdmBnZeXnDJfWChrlRHZEYM68mi6oO6vjFIUdOwuv5ob8frrQJcuwO6dpcD27cC5c1AG1kG/9QkY96A/3lsSiBaKbKT3Y0BH5Oq0Wjlpwhir8lHq0pt89pmV+VKIzMOgjjxa5Cez0LvpSYyN3AEVQgxTmyQnA7NmYesWgVdfFcjOBg7d/ZycBfH++3LQjZcXFKVahHmpIIQCn33msFMhIhuxeT7K226TM7MKC4FPPrFJG4mMYVBHnk2pxPaTzbCiw2togtyyJMTXE4meuxqE8cOuoLRUgQexBA/kpcjpr/ffD9x7rxwv4+uLiaWyo169GlCrHXc6RFRzNs9HqVDIS3sA8N57QHGxNc0iqhaDOvJsiYkyYehPP8n327fLb9UzZ6J0VhLuy3gOuflBaK/IwvujtgDr1gH//Qe0agV8+aXcV6NBjxf6oi0OorAQ+OorR54QEdWUXfJRjh8PNGggLwGuXWtVu4iqw6COKDERpYmv4hDaQJw/D6SlAV274g3fRPz0E1CnjsCq3TEIXLMUGDECePPNsnJAiYnA7t1QfLQQE/2WAwAWJ9WkeCQROVpcHBAVdT1NkRFW5aP09weefFK+/uCDGreRyBgGdeTxSkqARh/NQjscwgk0AxQK7Bjxpn4S6/vvK9AhtlzV74rlgDp3BkJDcb9mEbwVJdiTE4nMzNo/DyKyDaWybKKqscBOCCAlBZbno3ziCfmFcPXqmjaRyCgGdeTxvL2BZj7y6trbimnYIvqg9ZU/cdddwH33GSkVNGuWYdqSOnWAefPQAOcx1Os7AMDSpbXTdiKyj/h4YM0amd6kIoXCylKADRoAs2cDjRrVuH1ExiiEsLhssdNQq9UICQmBSqVCcFW1m4iqkDp+NR74ajCuIEi/LAqnkDJ2F4YsHQM/PzMOIgTQqxd+26XAobhH8b/vHkLduvZrMxlyx77AHc/JFWm1cpZrbq4M5BYtktUlWrQA9u9Hzf7OtVorLveRJzK3P+CVOvJoqeNXY/RXo3AFhj1zDqIw5utR2PSgmbdJFApgwQL0wk48vH0i6h7db4fWElFtUyqBfv3kPId+/YAPPwRatgQeflgOk7PKrl3AgAHACy/YsKVEDOrIg2m1wJTvBkJAAcBw4IxuWcJ3A81PMNqzJzB2rLxq9+yzZVlKichthIYCmZnAyy/LoRtWuXwZ+OUXmbOOOZDIhhjUkcdKTwdOq0NQMaDTEVDglDrE/ASjADBvHkRAIN4tfgI33SSqTGBKRK6p/BW6a9eAixctPMCgQUC7drIG2eLFNm0beTYGdeSxbJ5gFABiYqA4dRKpXqORsd8Ln39uTcuIyBX8/TcQGyvzkFt0YV6hKCsd9u67FtQbI6oagzryWHZJMAoA4eGYOFG+/OwzWXSCiNyPQgEcOQL88IMca2eRe+8FIiKA7Gxg/Xo7tI48EYM68lh2STB63ahRQHBdLY4fB7Z8wzEzRO6ofXtg/nz5+vnngYMHLdi5Th1g0iT5+v/+z+ZtI8/EoI48VlUJRnXvrUowCiCgjsB431QAwOIZ/1rdRq0W2LJFplDYsoV3aYiczeTJwMCBcmzdvfcCRUUW7FxYCHh5ATt2AL//brguOVnmxCSyAIM68mimEoxGRcnl8fFWHlihwMRXowAAaw91QN7eoxYfIjUViIkB+vcHJkyQzzExcjkROQcvL2DJEiA8HPjjD1kwwuwvYqGhcnxGfLysTKOTnCwPxBx2ZCEmHyZC5QSjcXE170+FAG4MOY4D+c3xwY2L8GTGY2bvm5oKjB5defC17gpijQJON+SOfYE7npM7S02Vwy4qioqSdwRM/r3qAjhd6cGK74lgfn9gbZYdIreiSzBqSwoFMGmKP36dsxad9y8DtrYB+vatdj+tFpgyxfhsOiHkcRMSgOHD+UWeyNnl5MgvaCa/iOkCt5kzgTlz5P1bBnRkJd5+JbKjJ5MbY80Tv+BW7JAJic2YCpuejirz2wkBnDoFy/LnEZHd6L6IGaP7cpaQUMWt2OnT5Te0oiL5ra1FCw6gJaswqCOyt8BAwM9PDrhZXaHsmJHB0OYmLLYofx4R2U2Nv4i9/npZECeEnHHRvj3w5ZdASYnN20vui0Edkb0FB+OwpikSW67AtTtHli03Mhj611/Nv+ticf48IrKLGiUyT04GXn1V3nJVq2VNWAA4fBi47z6gQwfgiy945Y7MwqCOyM5KX07EHSF7MOfoeMwdtgsrl5diy8PLoJ05Sz92Jjtbjru5/XaZi9RU7jygZvnziMj2rE5kXnFSRFAQ8PPPsrAsIHPZHT4sg77SUnlVPznZ+MGZAoXAiRJEduflBXS/IxQn1wDJW/sAWwHgfkR5D8T//bsbBx46iflfRePaNQW8vIAnngBic9bhofUjAIXCYMKEQgFACKT0XAWlcqxjToiIDOgSmefkGJ/gpFDI9ZW+iGm1xidFzJkjh2wUFgIhITKXkY+PvKo/cybw118yZ4r39f/CyweH5NmEBQ4cOCBuvvlmERoaKp5//nlRWlpa7T7z588XDRo0EEFBQSI+Pl5cuHBBv27IkCECgP5x++23W9IcoVKpBAChUqks2o+oNq1dK4RCIYTs7sseCmiFAlrRFb8LQIh+dX8X+/83R4gzZ4RIShJrMVJEBV822Cc6+LJYi5FCJCU5+rScijv2Be54Tu5M93du9G9dIdfbxLBh8qDh4UIsXy77AoB9gpsztz8w+/arRqPB0KFD0a1bN+zduxdZWVn4vJpq5du2bcPSpUuxbds2/PHHH7h27Rqee+45/fp9+/bhwIEDyMvLQ15eHjZs2GB5VErkxKpMT3J99EOuVzS+wv/w65VYdF71irzlkpiI+KSbkK0OR6/gTADAMz1247g6HPFJNzHdAZGTMZXIPCDAxnklhw2TB710CbjnHua0IwNmB3Xff/89VCoVFixYgJYtW2Lu3LlYvHhxlfvs2bMHd911F9q0aYNWrVph/PjxOHz4MADg9OnTEEKgY8eOCA0NRWhoKAIDA2t2NkROptpZcfBCbmlDNPzqPShWrQJee01mmQeAxEQom0UjTr0RAKDd9TuUSbPYeRM5qfh4OSY2LQ2YMUMuCw8HRo6scjfLTJwI/PefHNcByHu77BPoOrODuv3796NHjx4ICAgAAHTu3BlZWVlV7tOxY0ekpqbi6NGjOHfuHBYvXow77rgDgAz4tFotoqKiEBgYiHHjxiEvL6/K42k0GqjVaoMHkTMze1ZcaUNgzBjgpZcMV4wZg7Y4BAA4pGjPzpvIyekSmb/yihwGd/o0cOyYjT/k//6vLOelEEC5O2Dk2cwO6tRqNZo3b65/r1AooFQqqwzEBg8ejNatW6NVq1Zo2LAhCgoKMH36dADA4cOH0a1bN/z444/Yu3cvsrOz8VLF/9AqeP311xESEqJ/REdHm9t8IoewelacjlarD+oOijamZ74RkVMJCAB69ADatgXOnrXhgctPiujfXy5bsIB9AwGwIKjz9vaGn5+fwTJ/f38UFhaa3GfVqlU4ceIEDh06hIsXL6Jjx4649957AQDTp0/H999/jw4dOqBdu3Z44403sGbNmirbMGPGDKhUKv3j1KlT5jafyCF0s+JMpSipMj1JcjLwf/+HDoEn8BEex4q7lsvOnJ03kUv46Sfg4EGgd28bHbBiCpRHH5XLGzZk30AALAjqwsPDcf78eYNl+fn58PX1NbnPypUr8cQTT6BNmzYIDw9HSkoKUlNTcfny5UrbhoaG4sKFC9BoNCaP5+fnh+DgYIMHkTNTKmUxb6ByYKd7n5JipIZruc47aHBvPI5F6NtHyM6cnTeRS/D3t/EBK6ZAGTlSVp04flwuZ4Jij2d2UBcbG4tdu3bp32dnZ0Oj0SA8PNzkPiUlJfjvv//073OvDzDSarUYPXq0wfF+//13NGrUqNLVQCJXZ2pWXFRUFbPiynfevXrJZb/9Jt+z8yZyKUVFMuVcjc2aZTiu1t9fzoC9PmOeyYfJ7KCuT58+UKlUWLZsGQBg3rx5GDBgAJRKJdRqNYqLiyvt07t3byxatAgfffQRli5dinHjxqFnz56oV68eOnfujKlTp2L37t3YuHEjEhMT8eSTT9ruzIicSPlZcStWyOfjx6tIc1C+8+7VC0d92+Gz0wPx/fdg521HmZmZiI2NRVhYGKZNmwZhLBdNOUIIzJ8/H61bt0ZERAQmT56MgoKCStsVFxejU6dO2LJli51aTs5qxgwgLAyoJgNYzQkho0fybJYkv1u3bp2oU6eOaNCggahXr57IzMwUQgjRrFkzsW7dukrbX716VTz99NOiSZMmwtfXV/Tt21ccOXJECCFEUVGRePjhh0VQUJBo2bKlmD17tiguLrakOUzOSZ6hpES883axAIQYOdLRjXFOtugLrl27JmJiYsTjjz8ujhw5Iu666y7x2WefVbnPJ598IiIjI8Xu3bvFoUOHxM033yzuvffeStvNmTNHABBpaWlmt4f9m3tITpa5gUeNsuOHfPWVEDfcIMTcuXb8EHIkc/sDi4I6IYQ4ffq0WL9+vTh37pzVjbMVdnrkKX78Uf7H0Lato1vinGzRF6xbt06EhYWJgoICIYQQGRkZonfv3lXuExcXJ1JSUvTvN23aJIKCggy2OXz4sAgNDRUxMTEM6jzQjh3yb7dePSG0Wjt9yJIl8kNatLDjh5Aj2byihE5kZCSGDx+O+vXr2/B6IRFVpV07+XzkiICRkQ5kA9bk4rxw4QKaNm2qf69UKqGsMOvl8ccfx/Tp09GsWbMqj8U8nO4pNhYIDAQuXgQOHLDTh4wZAwQHy4R4aWl2+hByBRYHdURU+yLPZyDQqxAlJQrbJzIlANbl4uzSpQvWr1+vf79kyRIMHDjQ4L1KpTIoj2gK83C6Jx+fspRFdou3AgPlhAkA+OQTO30IuQIGdUQuwCuqCdqUHgQAHNp3xcGtcU/W5OKcO3cu9uzZg7i4ONx44434+uuv8dRTTwEAzp8/jxkzZmDx4sXw9vau9vOZh9N93XabfP71Vzt+iC5nXWoqUCH9GHkOBnVErqBBA7SrK4vIHtqc4+DGuCdrcnHGxMQgKysLixYtQtOmTXHHHXcg7vplmYSEBEycOBFdunQx6/OZh9N96Qo/bN0KlJTY6UNuugno1g0oLgauZ6kgz8OgjshFtG0l/zc4uM8WCa+oImtycQLyNm1wcDA2b96MefPm6ZevWLEC7733HkJDQxEaGort27djyJAhBtuQZ7jpJmD4cJnexK5ZRx57TD5/8olMcUIep/p7AkTkFMbFF6FHxgB0DA4DsNrRzXE75XNx3n///ZVycdapUwc+Pj5G950zZw7GjBmDrl276pcdP37cYJtx48YhISEBgwcPtut5kPNRKoFyQy/tZ/x4YMcO4MEHa+HDyBkxqCNyEa1GdESrmb8AGXXlPRwzxmmR+by9vbFo0SJMmDAB06ZNg1arxdatWwHImbApKSkYMWJEpf2OHDmCFStWIDMz02B5TEyMwXt/f380atQIoaGhdjoD8nhBQcDSpY5uBTmQQgjXvUarVqsREhIClUrF8Sfk/rRaIDwcUKuBP/8EzByr5Qls2Rfk5ORg79696NWrl0NTN7F/cz9nz8oZsKNHy1mxROYytz/gV30iV6FU4psec7H7bFOMyw5Cpy6ObpB7ioyMRGTFQr1ENSQE0KkTcOEC0KxZWUlnu/jnH+Cjj+QXvwcesOMHkbPhRAkiF/KJ72TM/Wsotue2dHRTiMgCCgXQt698bdfUJgDw449ASgqwYAEnTHgYBnVELqRtW/l88KBj20FEltPlq7N70Yd77wX8/IC//gJ+/93OH0bOhEEdkQvRlQs79LsaUKkc2xgisoguX92OHcC1a3b8oPBwOXAPAD791I4fRM6GQR2RC9FdqTu06zKwebND20JElmnbFmjUCNBogHIpEe1DV2Fi5UrgCqvQeAoGdUQuRBfUnUJTXNmy17GNISKLKBRlV+vsPq6uTx/ghhtkQPfVV3b+MHIWDOqIXEh4ONAg+CoA4J8tuQ5uDRFZqtbG1SkUwCOPyNeffGLnDyNnwaCOyMUYTJaw68AcIrK1u+4CvvwS+Ppr64+h1QJbtsg7q1u2yPdGPfAA0KQJEBcna8KS22OeOiIX8+FifwT2j0XTC/uAfY8CvXs7uklEZKYmTYB77rF+/9RUYMoU4PTpsmVRUcA77wDx8RU2btAAOHUK8OL1G0/BnzSRi+nQUYGYPk3hBQH89pujm0NEtSQ1VU5qLR/QAUBOjlyemmpkJwZ0HoU/bSJXpEtHv2OHY9tBRBY7fx6YPx9ISDB/H61WXqEzlktYtywhwcit2FmzgKQk4Oefgeu1jPWSk+V6chsM6ohczLVrwKyj92F8xwMomfKco5tDRBa6dg148UXg/fdlKWdzpKdXvkJXnhDyTmt6eoUVSiXw6qvAwIHAK6+ULU9OBmbOlOvJbTCoI3Ixvr7A/M8b4KvMjjgeFefo5hCRhaKjgVat5FW1SkGYCblmTnavtF1iIvD88/L19u1yhpUuoEtKkuvJbTCoI3IxXl5Amzby9aFDjm0LEVnH0nx1jRvXYLs33yzrNDp1YkDnxhjUEbkgfWWJ1QeANWsc2xgispguqDM3X11cnJzlqlAYX69QyCuAcaYu3qekyGetFvDxYUDnphjUEbkgfVD3xR75jZuIXIouqMvIAC5dqn57hUKmLdG9NiYlpYohcnv2lL0uLuYECTfFoI7IBekTEKMdkJkJqFSObRARWaRRI6BdOznBoeKk1IrOnQO6dgUCAuSF+chIw/WhobISWKU8dTrJyXKyxIsvAhERctns2XI5uRUmHyZyQe3ayedDXu0hSgUUu3YBgwY5tlFEZJH+/YHs7KpntV67BowYAezfL1OaZGYCw4fLCRa5uXIMXVxcFVfoKk6K6NpVXqWLi5PLAd6KdSMM6ohcUOvW8hbMFdTFJYSj3m+/MagjcjHJycCCBYCfn/H1QgAPPwzs3Cmvxm3YIIfDAUC/fmZ+iFZrOCnif/+TUaKvrxykZ7LGGLkiBnVELqhOHeDff4GmP3wGn6cusbIEkQsKD696/ezZsr6rtzewdm3ZsIuKCgvl7VcvL+DBByusNDZ2ztdXPicmmjegj1wGx9QRuaiWLQGfuB7yza5d/MZN5MIq/vmuWCGDOgBYuBC47TbT+65fD0ycKHMLl5SY+YFCAG+8ATRtCuzda02TyQkxqCNyZR06AEFBwJUrcrANEbmU1FSgfXvgySfLlmVkAA89JF9PmwY88kjVxxg1Ss5/yMkBNm4084MVCuDPP4GCAnl5T6MxWK3VAlu2yCuFW7bwO6OrYFBH5KIyMoD7HlQiYUAmcOwY0Lmzo5tERBby8ZFFHjZuLAug2raVcdaIEcC8edUfw89PXqkD5FU9s73/PtCgAfD332WXBSEDzZgYOZFjwgT5HBMjl5NzY1BH5KLy84EvvwTW/9EUaN7cdPIqInJaly/L5zNnygKo1q1lqdavv5bj5Mzx+OOyC/jpJzne1iwREcBHH8nXb7wB7NmD1FRg9OjKM3JzcuRyBnbOjUEdkYvSpTU5cUIOlCYi15KaCjzwQOXlOTnAmDEW3EqF/F53553y9ccfW9CIkSNlNFlaCu0DD2PKMwJCVN5MtywhgbdinRmDOiIXFREB1KsnXx9O/EJmHj171rGNIiKzaLUy75wtA6gnnpDPS5YAV69a0Jh33wUaNkT6oQiczjF9xV8I4NQpmSOPnBODOiIXpq8ssToTWLeOqU2IXER6etVJh60JoO68U459i40FLlywoDH16gEff4xcZbRZm+fmVlgwa5bp6hTJySxJVosY1BG5MH1liQZ95AsGdUQuoVJgVMPtAFlVIjMT+OEHINq8+KzM8OFovHKBWZs2bmzkg2fOrBzY6apZmCx3QbbG5MNELkx3pe6QsoN8waCOyCVUCoxquJ1OYKDlbdGJi6+P+vXlVT5jt4UBGSzGxVVYqKtWMXMmUFoq68xWLE9GtYJX6ohcWNu2cnZcYWB9uWDfPlkskoicWlycrNJlatK6QmEigDJTTg7wzTeW7ZOdDVzJK4IQAgqUGt1mTNRvZRfeSkpkbqWFC+WU27AweavVy4sBnYPwSh2RC7vjDjnz1c83AGjUADh3DvjjD6BXL0c3jYiqoFQC77wj04QoFIZXxnSBXkqKdXcuDx4EOnWS+evOnAFCQqrfR6ORZWGvlviiLQ4iH3WRg7J7uMF+16DW+CPjbGMIASgKC+RlxPz8ygcTQgZ2DOhqHa/UEbkwX9/rxcAVCqB3b7lwxw6HtomIzBMfD6xZA0RGGi6PipLL4+OtO27btvJRWAgsW2bePs8/L78P1qsH/DT5G5xADNLQDytuXoC0lo/gnCYYKeGz8d3B5jLoDAyUDQ8Olt8uZ84E7rtPHszLS96G1Y2xO35cJtAzdU+XbIZBHZG76NVLRnkWTXsjIkeKj5e3PdPSZL3XtDQZA1kb0AHyO54uvcnChdXHUmvWyOISgAwCo99/Ecp7J6AftmL83ufQ7+hi+KEYUzRvws+7XI6VX34BLl2SAZu3N/DFF/KWq1Yrn3WTJ5KTgUGDgJ49ge++KxtzZwxny9aMcGEqlUoAECqVytFNIXKYhQuF6NlTiI9Srgpx9aqjm+MQ7tgXuOM5Ue1RqYQIDBQCEGLLFtPbHTkiRHCw3O7FFyusVCrlCqVSiE2bhLh4UQghRHGxEC+/LMTq1de3S0qS2yUlGe6vW967txB16sjXgBBNmsjn2bONb1/xOGR2f8ArdUQuLjcX2LkT2PtFFuDvX3kDY998mVeKyK0FBwP33CNff/ih6e1mzgTUajl6w6BLSE6WV9x8feXzvn1AeDgA4NNPgddek/Vmjx5F2ZW5imPoEhPl8gED5OXHadOAgAA50A+QV+zuvbfs8zi5ouZqKci0C36TJRJixQr55fZWbCv7hltaKp+r+wZt7nIn5459gTueE9WuP/+Uf87e3kLk5hrf5soVISZPFuLkyXILK/YDFd4XFcmLb4AQXbsKce2aBY06d06IGTOEqFu37Mqdr69L9ju1ydz+gEEdkYv74w/ZH0YEXJEvGjWSvXR1AVo1Hbcrcce+wB3PiWpfz55CBAQIsXGjmTuY+YXv5EkhwsPloqeftqJhFy8KMWtWWUDn62vFQTyHuf0BU5oQubg2beTzhcJAXBj5KCLWfQJ88IFceMMNwJ49wNChwEMPlY2+PnxYLr/hBnnLY9YsOVuNtz6I3Mpnn8nMI3XrAlu2yOEapaXy+dln5URVA1XdStWth8yht2wZMGQI8N57QN++wIgRsqxZbq78zLi4KlKyhIfLDy8qkrd4i4rkLVj2PzWiEMJ15xir1WqEhIRApVIhODjY0c0hcpiYGODECSD9mzzcOizc+Ebz58sxLYAM6G65xXC9UimTibogd+wL3PGcyDFSU4EpUyrXmh01Ss58rYkXXgDefFMOlQsOBs6eLVsXFSVz8RmdyZucDO3MWUh/aAlyIzqh8Vf/h7hTy6FMmsXAzghz+wNOlCByA/pyYYu3yxfe1y/CDx8uRzV/+qlMKaDTvLlcNnx42TKt1vTkCSJySampMsFxxYBOty41tWbHf+01ecG/sNAwoANkVYvRo418RnIyUmf+iZjgS+i/5H5MePMm9D+1DDF+uUid+Sf7oRpgUEfkBtq1A2JC81C64Vt566S4WD5v2CBnmk2cCHTuXLZD/fpy+YYNwJgxclnTpsaLchORS9Jq5RW6qu7HJSTo76haxcvLeFEJoOxzK35GauYNGI21OK02LHWRo4nAaKxFauYN1jfIwzGoI3IDC8KScfxyOB5Lii67daFLJ2AsUCufPmDGDLmsoMD09kTkctLTjV+h0xECOHVKbleTz8jNrf4zHn4YeOstYONGYMpvYyFQueituB6SJOwcW6NA05NxogSRG1CUmje4Wa/8YOiCAmD2bHkPd/Ro49sTkcupKtiyZrua7KsrV9a3bzWBJhT6QLNfP+vb5akY1BG5g3LJgoUoKwgOwPig4/LJhQMD5dW5qrYnIpfTuLFtt6vJvsOGyYkUSiWwdWv129ck0PRkvP1K5CaGDwcaNAAOHHB0S4jIGcTFyRmoisp3OgHI5dHRcjt7f0ZqqiwN++CD5h23JoGmJ2NQR+Qmzp+Xj0OHrNj5v/+ATZtqNriGiJyKUilTigCVgy7d+5SUKnLJ2eEzqg0CUYroegU1CjQ9GYM6IjehS2ty8KAVO69ZI7OIvvWWTdtERI4VHy//vCMjDZdHRcnlRnPI2fEzqgoCATl2JGVRYI0CTU/GMXVEbqJdO/ls1ZU6faI7a3YmImcWHy+HZ5hd7cHOn6ELAisnRFbggw9sE2h6KgZ1RG6iRnGZrtbYsWMyx52Pj83aRUSOp1TafzapJZ9RMQhcsADIyJCTKaBWAytXAiNHyoHCZDYGdURuQhfU/fOPrO1YqaZjVSIj5SzYggLg6NGygxER2Un5IPCmm4DQUKBRIwC3jQDS0mRwpyttSGbhmDoiN9G8uayLffUqcPKkhTsrFGVX6/75x+ZtIyKqStu21wM6ALjnHvm8aFHV5TCoEgZ1RG7C2xu49Vaga1dg9Wpgy5bqcwhrtXK7lSuBLWEjoYUXx9URkUP9c9M4iLpBwJEjsoMis1kU1GVmZiI2NhZhYWGYNm0ahBkR9JtvvomGDRsiODgYo0aNwsWLF/Xrtm7dinbt2iEiIgILFiywvPVEpJeaChw+DPzxB/DCC0D//kBMjOmC3ampcn3//sCECUD/X15BDLKR+mNgbTabiAiAvCg3fDjQtlsg0vonyYWffOLYRrkYs4M6jUaDoUOHolu3bti7dy+ysrLw+eefV7nPtm3bsHTpUmzbtg1//PEHrl27hueeew4AcP78eQwbNgzjx4/Hzp07sXz5cqSlpdXoZIg8VWqqrPBVsfxOTo5cXjGwM7k9ojB6y2STgSARkb0oFGVpUd678qB8sXYtcOGCw9rkaswO6r7//nuoVCosWLAALVu2xNy5c7F48eIq99mzZw/uuusutGnTBq1atcL48eNx+PBhAMDy5cvRuHFjJCYmonXr1pg5c2a1xyOiyrRamRrA2IVz3bKEhLJbsVVuDwUAhcH2RES15amn5PM3W0OR3XEIUFRUVjiWqmV2ULd//3706NEDAQEBAIDOnTsjKyuryn06duyI1NRUHD16FOfOncPixYtxxx136I932223QXE9+2D37t3xxx9/WHseRB4rPb2aAtkCOHVKTqK45x7zt2dxCSKqbe3bAwMGyBn8HzaZI6fxHz/u6Ga5DLODOrVajebNm+vfKxQKKJVK5OXlmdxn8ODBaN26NVq1aoWGDRuioKAA06dPN3q84OBg5OTkVNkGjUYDtVpt8CDydOYWvi4tlQ9zt2dBbSJyhGeekc+f/t4ZhVnZwHvvObQ9rsTsoM7b2xt+fn4Gy/z9/VFYWGhyn1WrVuHEiRM4dOgQLl68iI4dO+Lee+81erzqjgUAr7/+OkJCQvSP6Ohoc5tP5LbMLXy9Zg3w4Yfmb8+C2kTkCHfdJVM05eUpsCKd/89bwuygLjw8HOfPnzdYlp+fD19fX5P7rFy5Ek888QTatGmD8PBwpKSkIDU1FZcvX650vOqOBQAzZsyASqXSP06dOmVu84ncVrUFshVAdDQwYgQQFmZmQe2gPBbUJiKHUCrLxtatXHl94YkTMhkxVcnsoC42Nha7du3Sv8/OzoZGo0F4eLjJfUpKSvDff//p3+dev5+j1WorHS8jIwORFasBV+Dn54fg4GCDB5Gnq6pAtu59SkpZDcYqt4ecPZFyw0IW1CYih3n4YWDpUmDTJgBTp8pLd9Vk3CALgro+ffpApVJh2fVZKPPmzcOAAQOgVCqhVqtRXFxcaZ/evXtj0aJF+Oijj7B06VKMGzcOPXv2RL169TBs2DBs374daWlpKCkpwVtvvYVBgwbZ7syIPIiuQHbF70VRUXJ5xQLZprYPCyrBGoxGvPpzu7aXiKgqoaHA/fcD/v4AbrhBzuBihYlqKYQ5GYSvW79+PSZMmICgoCBotVps3boVHTp0QExMDFJSUjBixAiD7a9du4YXXngBa9euxYULF9CzZ08sXrwYLVu2BAB8+OGHSEhIQEhICAIDA7F79240bNjQ7Mar1WqEhIRApVLxqh0RZBoSXYHsxo3lrdaqrrjptv/lF6BePeChwbkIaddE7lRYKKfMugB37Avc8ZyIrFGap8K1yJYIuHoR2LED6NXL0U2qdeb2BxYFdQCQk5ODvXv3olevXqhfv36NG3rkyBEcPHgQffv2tbjjYqdHZGNCAMHBwJUrQFYW0K6do1tkFnfsC9zxnIgstW4dMG0aMCZgE14/MAR44AGPvA1rbn9gce3XyMhIDB8+3CYBHQC0atUKQ4cOZadF5AwUCllZG/DIGrCWlkIUQmD+/Plo3bo1IiIiMHnyZBQUFOjXL1q0CI0bN4aPjw8GDhyoH1dMROZRKICjR4FPDvfFVfgDq1YBly+XbZCcDMyaVXnHWbPkOmNM7eMGLA7qiMg9ffONHI+cOXkhsHMncD1RuKewphTi4sWL8e6772L58uXYsWMH9uzZg0mTJgEAtm/fjsTERHzxxRc4fvw4rl27hueff74WzoTIfQwdCjQLvYyLmrpYGfQ4cPUqsHy5XJmcDMycaXyMiVIp11UM7Kraxx0IF6ZSqQQAoVKpHN0UIpd3991CAEK8+66jW2I5W/QF69atE2FhYaKgoEAIIURGRobo3bt3lfvExcWJlJQU/ftNmzaJoKAgIYQQn376qVi7dq1+3WeffSZuuOEGs9vD/o1Imj9f9k1d8IcoBYTo2VOIpCS5MCnJ9I4VtzFnHydlbn/g7diQkoicRffuMn3Anj2OboljWFMK8cKFC2jatKn+vVKphPL6FYCJEycabPvPP/+gVatWJo+l0Wig0Wj071kxh0iaOBF49VUg4+pN2IHeuHXf7/JuwtixwIEDwP/+Z3xH3VW6mTOBOXNkHdmkJCAxsfYaX8sY1BERACA2Vj7v2V0KLPxY1lt84w3TWYrdTFWlEMPCwozu06VLF6xfvx4jR44EACxZsgQDBw6stN3Fixfx8ccf48svvzT5+a+//jpmz55dw7Mgcj/h4bJu9aefAq8oXsPjRR+hsfcFxLXvBOWrr5jeMSEB2pcSkZ60DblFEXKflxLhpjdeAVgx+9WZcHYYke1cuADo5j/lIQyhuAz89x/QoIFD22UOW/QFL774IoqLi7FgwQL9sujoaOzatctkYvTs7GzceeediIiIgFqtxl9//YVt27YhrkI5jrFjx+LKlSvYtGmTyc83dqUuOjqa/RsRgAULgOeeM1wWFXgJ74zfjfgux4zuk+o/AVOe9cJpdUjZPsEqvLMkpFLuTmdnbh/HK3VEBACIiABatACOHQP2NhqCAWe/lDNgXSCos4Xw8HBkZmYaLKuufGFMTAyysrJw6NAhvPDCC2jYsGGlgO6zzz7Dtm3bkJGRUeXn+/n5VaqvTURAairw/HO6609ldw5yCkIx+tPBWLNWUSlIS00FRo8SEDC8bpWjDsLoUcLoPu6As1+JSK97d/m8J+h2+eKffxzXmFpmTSlEQN6mDQ4OxubNmzFv3jyDdXv27EFCQgK++uorixKrE5Gk1QJTHlJdD84Mh4IIeAEQeOZBNS5cAPLy5EOtLr+Pl9F9Eh5SQautrbOoPQzqiEhPF9Qd8u5w/YXn5KqzphSizpw5czBmzBh07dpVv+y///7D0KFD8eKLL6Jbt264cuUKrly5YvfzIHIn6em4fvvUeLgi4IWc/GDUry/H3unG31W3zyl1CNLT7dduR2FQR0R6990HnDoFLH16n1zgQVfqvL29sWjRIkyaNAkNGzbEmjVr9FfeOnfubHI83JEjR7BixQq89tprBstXrlyJc+fO4ZVXXkFQUJD+QUTmsyZfd2Gh/Y7t7Dimjoj0IiKuv2jnmVUlRowYgX///bdSKcTs7GyT+7Rq1QoqlarS8oSEBCQkJNippUSeoXFj87b7+Wegb1/5ets24NdfbXdsV8Kgjogqa9NGPh8/Dmg0gAcN4I+MjDQ525WIaldcHBAVBeTkyNLUFSkUcn3//mVFIvr1q24fgagoBSrMaXILvP1KRAa++w4Y9ngjzJ90DDh7Fqhi9icRkT0plcA778jXFVNm6t6npBhW/apyn+uzYSvu4y4Y1BGRgTNngG+/VeDHw81l4joPST5MRM4pPh5YswaoeAE9KkouN5aaxNQ+TSIVWLPGPdOZAAzqiKgC3QzY338HSksd2xYiIkAGadnZQFoasGKFfD5+3HhAZ2yfevXksk8/rXofV8cxdURkoH17ICAAyM8H/pk4H+06+wBTpzq6WUTk4ZRKOV7Omn2mTJF9Wkzxv8Dc1cCgQUC3bvZopkMxqCMiA97esq9LTwf2fP432nU/xKCOiFxaYuL1Fw++BixdCly65JZBHW+/ElElsbHyeQ+6y1x1rlsimoiozN13y+cq6jC7MgZ1RFSJvlwYugMqFfDff45tEBFRDeXnAz8q70K2sqXMwXnkiKObZHMM6oioku7dgTp1gGD/YpkAwMOSEBOR+7n/fmDwqECsinlBLnDDq3UM6oiokpgYWRT7l9tekyW0PahcGBG5J12y4XS/2+ULBnVE5AkUCjlhQl9ZglfqiMjF6YK6HTkxKIUC2LJF3pN1IwzqiMi0tm1RDG+ZEIqIyIXddBMQGAjkqZT4O/pOWf7w778d3SybYlBHREb99RfQ4f8mossNhcC6dY5uDhFRjXh7Az17ytfpD30GXLgA9Ojh2EbZGIM6IjKqcWMg65ASWYd9oFKzVBgRuT79uLrDDeWVOjfDoI6IjKpfX06YAIC9ex3aFCIim9AHdenl0m+WlDisPbbGoI6ITNLnq3vmC+CnnxzbGCKiGrrlFuDDD4HvvwewbJmcDDZnjqObZTMM6ojIJF1Q93tWILBnj2MbQ0RUQwEBwBNPAJ06AQpRChw+DGzc6Ohm2QyDOiIyyaCyBNOaEJE7ufNO+bxvH3DmjGPbYiMM6ojIpK5dAS8vgRxEIefAJUc3h4ioxtRq4OOPgeffbFj2zfW77xzbKBthUEdEJgUGAsNvy8cD+BxFR06WG1lMROSaSkqASZOAt98GzvX7n1zoJtUlGNQRUZVSN/njc+UjaF74N5Cb6+jmEBHVSHg40LGjfL29/kj54uefgWvXHNcoG2FQR0RV8/UFWrSQrzmujojcgD61yenmQJMmQEEBsHWrYxtlAwzqiKhaJa3b4S+/WJSePefophAR1Zg+qNuuAB58EHj0UaBRI4e2yRa8Hd0AInJupaVA493rcEHjhUPdgDaObhARUQ3pgro//wTy015DUJBj22MrvFJHRFXy8gJuaCO7it9/d3BjiIhsICpKVswpLQV27nR0a2yHQR0RVUufr475h4nITcTFAQoF8M8/ALRaYNcuYPt2RzerRhjUEVG1uncpAgDsWfwXUFjo4NYQEdXc3LlAXh7w9NMAPvkE6NkTeOUVRzerRhjUEVG1uvf2AQD8WdgGRVlHHNwaIqKai4oCQkKuvxk0SD5v3y4jPRfFoI6IqtWipQLh3ioUwQ9//fyfo5tDRGRbzZsDHTrI27A//ujo1liNQR0RVUuhAGIbngQA7NlR7ODWEBHZxvLlQO/ewIIFAO6+Wy7cuNGhbaoJBnVEZJb7444jGa/g1tJtjm4KEZFNnDsH/PYb8MsvAIYMkQu//15esXNBDOqIyCwTxgm8gtfQ+b+fHd0UIiKb0OWr2/HLNWg3pwFhYcClS3ImrE5yMjBrVuWdZ82S64wxtY+dMagjIvO0uZ52+J9/ACEc2xYiIhvo0gWoWxdQafyRmbRWzp4AysbVJScDM2cCSmXlnZVKua5iYFfVPnbGoI6IzNOyJc7UaYl14RPx7z61o1tDRFRj3t4ykwkApN89DzhwAHjkEeDVV8uCs6QkIDGx8s6JiXJd+cCuun3sjGXCiMg8Pj5IuPtfrF6jwLxfgBdvdnSDiIhqLi4O+PlnID3wTjylC9KWLQOKioDYWCA3F3jyScOdFArggw/KAjddIFdS4rCADgAUQrjufRS1Wo2QkBCoVCoEBwc7ujlEbu+tt4Bp04D4eGDtWke3pow79gXueE5EzmjLFqB/f6BxYyAnB1D4+8mATqEwPdTEy8twMoVSKWuOAcD//gfce6/Mfefra5M2mtsf8EodEZktNlY+b9sGrFwpO8G4uKqHjmi1QHq6/LJrzvZERLXplluAJk1kOcSCxHmoW1Qkg7GiIqBfP6Bv38o7KRRlr5OTZUCnCwJXrZKPevWAsWOB++4DevSQEyeUSuNX8ZKTZWdZ08kVwoWpVCoBQKhUKkc3hcgjfPny3wIoFbLnko+oKCHWrjW+/dq1cr2521vLHfsCdzwnImdVWiqESEqSnVRSklxY8b0x5bcpLRVi0iT5PjCwrNPr27fq45nxOeb2BwzqiMgsa9cKoUBppaBOoZCPioHa2rVyefltq9q+JtyxL3DHcyJyWtYEXNXtc999Qtx7rxBffFG27sUX5bq77xbiwgXzAkdhfn/A269EVC2tFpgyBZCjSxQG64SQdx0SEoDhw+XdBf32RoajGNueiMihtFqI2Uk49UAimpZfrrtVaiwZsVZrfFJE+X0q3k6NjJTPmzYBjRrZfGIFJ0oQUbV0A4mr07KlzPl05Qpw9Gj126elySErNeWOfYE7nhORs9JogNatgVOn5PjfRo3s9EFnzwJffQU8+6z8huvrKz+8Gub2B8xTR0TVys01b7ujR4H9+80L6Cw5LhGRPfn5yWISALB9ux0/qFEjID+/LKArKjJdlcIKDOqIqFqNG5u33ZtvAj/9JJ9teVwiInvTlQxLT7fjh5RPTqzRVE5eXEMM6oioWnFxsnqOAsZHayggEN24GFOHHsEdzY9g6tAjiGpUXPX20WWdKBGRo9k9qDNWbcJYVYoa4EQJIqqWUgm88w4werQCClEKUe77oAzcBFJyx0LZdp3cHsA7GInRWAMFhMH2gJwpkZLCSRJE5Dx0Qd3+/YBaDdh8KKs5EytqiBMliMhsqalyVuvp02XLoqOBlPyJiC9dU3n74qGYUvp/OK2pb7B83DiZvNhW3LEvcMdzInJ2LVoAx48DL74IDB5sXrL02kiwzokSRGRz8fFA9iNzkIZ+WKG8D2noh+MT5yA+bzGgUlV6xBd+iewZH+u3fwVJAOS4uytXHHwyRETlpKYC//0nX7/xhpzxHxMjl1e1T0yM3HbCBPP2sScGdURkvuRkKGclol/S7Rhf8gX6Jd0O5axE02NBym+/4ynMQhJa4zAuXQIWLqzdphMRmZKaCoweDRQWGi7PyZHLjQVpun3K37mobh97Y1BHROaxdJBvxe1vuQXK2TPxEuYCAN6alY+rV2v5HIiIKqguWTogk6WXH/JmzT61gUEdEZmnqkG+SUmVey9j27/0Eu7pcQwxOA5xTYOsA7Xc4xERVZCeXvlqW3lCyKTEERFAw4byMWyYefvYNT2KERYFdZmZmYiNjUVYWBimTZuG6uZYzJo1CwqFotJjy5YtAIChQ4caLB8wYIDVJ0JEdjZrlulSNomJlcvhGNve2xs+yz/HN3XGIbu0Kbr9Mt8ODSUiMp+5SdAvXwbOnZOPCxdse2xbMTuo02g0GDp0KLp164a9e/ciKysLn3/+eZX7TJ8+HXl5efrH/v37Ub9+fdx0000AgH379uHAgQP69Rs2bKjRyRCRC2jRAp0+fAIBuCpvz+7b5+gWEZEHMzcJ+mefAQcOyMezz9r22LZidp6677//HiqVCgsWLEBAQADmzp2LyZMn46GHHjK5j7+/P/z9/fXvX3jhBUydOhUhISE4ffo0hBDo2LFjzc6AiFzPAw8Amzah9K9MfLcjDIM6Az4+jm4UEXkiXXL1nBzjY+QUCrn+/vvLUpW0a2fePrWdYN3sK3X79+9Hjx49EBAQAADo3LkzsrKyzP6gM2fOYN26dXj66acBAHv27IFWq0VUVBQCAwMxbtw45OXlVXkMjUYDtVpt8CAiF6RQAJ98goFNMjF0SgusWOHoBhGRp9IlVwdk11Se7n3FZOlV7QPIQM8RCdbNDurUajWaN2+uf69QKKBUKqsNxHQ++ugjTJgwAXXr1gUAHD58GN26dcOPP/6IvXv3Ijs7Gy+99FKVx3j99dcREhKif0RHR5vbfCJyNqGhGHin7PHmzgW0VzgVlogcIz4eWLMGiIw0XB4VJZfHx5u/j46XA6aiml1R4sUXX0RxcTEWLFigXxYdHY1du3Yh0tQZXae7Ivfrr7+iXbt2RrfZunUrRo8ejfPnz5s8jkajgUaj0b9Xq9WIjo5mxnUiF5WfD8TECFy6pMDK8MkYlzVTTi2zkDtWX3DHcyJydtZUh6i4z4YNwPLl8nHHHbZpl7n9gdlj6sLDw5GZmWmwLD8/H76+vtXum5aWhoiICJMBHQCEhobiwoUL0Gg08PPzM7qNn5+fyXVE5HqCgoCEp7SYmeSNOZeewP8mPgqvbzcYv59BRGRnSiXQr1/N9unVC3jhhdqfJAFYcPs1NjYWu3bt0r/Pzs6GRqNBeHh4tfuuWrUKI0eONFg2evRog+P9/vvvaNSoEYM2Ig/z9FRvBNfV4m90xIZNSuDjjx3dJCIiq/n6GgZ0ly/X3mebHdT16dMHKpUKy5YtAwDMmzcPAwYMgFKphFqtRnFxscl9f/jhB/Tv399gWefOnTF16lTs3r0bGzduRGJiIp588kkrT4OIXFVoKPD0FHl/IxmJEFOfBf75x7GNIiKygbVrgebNgY0ba+fzzA7qvL29sWjRIkyaNAkNGzbEmjVrMG/ePAAyQNu0aZPR/Y4ePYozZ84gNjbWYPmMGTPQvn173HHHHUhISMATTzyBGTNm1OBUiMhVJSQAgYECXr4+uHAtELjnHqCoyHCj5OTKCY6JiJzYtm3ySt0DD8gKE/Zm9kQJnZycHOzduxe9evVC/fr17dUus3AgMZH7OHwYaL1oGhRvvyUXvPQS8Npr8rWxurPluGNf4I7nRORpNBqgd2+ZY713byAtzbqcnOb2BxZPuI2MjMTw4cMdHtARkXu54QZA8dabwNixcsEff8jnagI6IiJn5ecHfP01EBwM7NghuzJ7Mnv2KxFRbVAv+gqpl4cj5oePket9HxprTyFuVjKUia84umlERBZr2RL49FPgf/8D5s0Dbr0VCAy0LG2KuRyQGo+IyLirV4GmTYGHfhyP/tiCCdov0B9bEPPpK0hNtf/nZ2ZmIjY2FmFhYZg2bRqqG50ihMD8+fPRunVrREREYPLkySgoKNCv37p1K9q1a4eIiAiDHJ9E5FnGjAGeeEK+HjYM6N8fmDBBPsfEwGb9G4M6InIa338PqFSVl+ecFhg92nYdnzEajQZDhw5Ft27dsHfvXmRlZeHzzz+vcp/Fixfj3XffxfLly7Fjxw7s2bMHkyZNAgCcP38ew4YNw/jx47Fz504sX74caWlp9jsBInJqffrI59JSw+U5ObBZ/2bxRAlnwoHERO5Dq5XfWE+fFgAqJx9WQCAqWoHjxyvfqrBFX7B+/Xo8/PDDOH36NAICArB//35MnjwZ27dvN7lPnz59MGrUKEyZMgUA8N1332HcuHFQq9VISUnBRx99hIMHD0KhUGDDhg1YvXo1vvzyS7Paw/6NyH2U9W/G1ysUsiSZsf4NsONECSIie0hP13V4xqtJCChw6pTczh7279+PHj16ICAgAIBM1ZSVlVXlPhcuXEDTpk3175VKJZTXe+T9+/fjtttug+J6dYzu3bvjD93kDyM0Gg3UarXBg4jcQ1n/ZpwQsEn/xqCOiJxCbq5tt7OUWq1G8+bN9e8VCgWUSiXy8vJM7tOlSxesX79e/37JkiUYOHCg0eMFBwcjJyfH5LFef/11hISE6B/R0dE1OBsicia11b9x9isROQVz6yTaq56it7d3pTKF/v7+KCwsRFhYmNF95s6dizvvvBNxcXFQq9X466+/sG3bNqPH0x3LlBkzZuDZZ5/Vv1er1QzsiNxEbfVvDOqIyCnExckxJTk58lZERboxJ3Fx9vn88PBwZGZmGizLz8+Hr6+vyX1iYmKQlZWFQ4cO4YUXXkDDhg0Rd72B4eHhOH/+vNnH8vPzY+1rIjdVW/0bb78SkVNQKoF33pGvFRWG1enep6TYLp9TRbGxsdi1a5f+fXZ2NjQaDcLDw6vcT6FQIDg4GJs3b9aXTjR2vIyMDERGRtq+4UTk9Gqrf2NQR0ROIz4eWLMGqBj7REXJ5fHx9vvsPn36QKVSYdmyZQCAefPmYcCAAVAqlVCr1SguLja575w5czBmzBh07dpVv2zYsGHYvn070tLSUFJSgrfeeguDBg2y3wkQkVOrjf6NKU2IyOlotXIWmLkZ123VF6xfvx4TJkxAUFAQtFottm7dig4dOiAmJgYpKSkYMWJEpX2OHDmCbt26ITMzs9IYuA8//BAJCQkICQlBYGAgdu/ejYYNG5rVFvZvRO7J0v4NML8/YFBHRC7Pln1BTk4O9u7di169etmkxvWRI0dw8OBB9O3b16K2sX8jIh1z+wNOlCAiKicyMtKmY99atWqFVq1a2ex4RESmcEwdERERkRtgUEdERETkBhjUEREREbkBBnVEREREboBBHREREZEbYFBHRERE5AYY1BERERG5AQZ1RERERG6AQR0RERGRG3DpihK6CmdqtdrBLSEiR9L1AS5c9bAS9m9EpGNuH+fSQV1+fj4AVCqiTUSeKT8/HyEhIY5uhk2wfyOiiqrr4xTChb/alpaW4syZMwgKCoJCoXB0c2qFWq1GdHQ0Tp065VFFvj31vAHPPXdLzlsIgfz8fDRp0gReXu4xqsQT+zeAv+88b89g6Xmb28e59JU6Ly8vREVFOboZDhEcHOxRfwA6nnregOeeu7nn7S5X6HQ8uX8D+PvuaXje1TOnj3OPr7REREREHo5BHREREZEbYFDnYvz8/PDqq6/Cz8/P0U2pVZ563oDnnrunnren89SfO8+b520LLj1RgoiIiIgkXqkjIiIicgMM6oiIiIjcAIM6IiIiIjfAoM6FPP3001AoFPpHq1atHN0ksoOLFy+iefPmyM7O1i/jz57cHX/HPQP7N/tiUOdC9u3bh02bNiEvLw95eXn4888/Hd0kuzL2x5+ZmYnY2FiEhYVh2rRpblXrEwAuXLiAIUOGGJwz4P4/+w0bNqBFixbw9vbGLbfcgoMHDwJw/583lXH33/GK2L+V8YSffW31cQzqXERJSQkyMzPRp08fhIaGIjQ0FEFBQY5ult0Y++PXaDQYOnQounXrhr179yIrKwuff/65w9poD+PGjcO4ceMMlrn7z/7o0aN46KGHMG/ePOTk5KBZs2Z45JFHPOLnTZK7/45XxP6tjCf87Gu1jxPkEvbt2yfq1q0rWrZsKfz9/cWgQYPEiRMnHN0su7n99ttFSkqKACCOHz8uhBBi3bp1IiwsTBQUFAghhMjIyBC9e/d2YCtt7+jRo0IIYXDe7v6z//bbb8XChQv173/99Vfh6+vrET9vktz9d7wi9m+e078JUbt9HK/UuYiDBw+iQ4cOWLlyJbKysuDj44PHH3/c0c2ym0WLFmHKlCkGy/bv348ePXogICAAANC5c2dkZWU5onl206JFi0rL3P1nP2TIEEyaNEn//p9//kGrVq084udNkrv/jlfE/q2MJ/zsa7OPY/JhF3XixAm0aNECeXl5bl0EWaFQ4Pjx44iJicFzzz2Ha9eu4YMPPtCvr1+/Pg4fPoywsDAHttL2yp93Re78sy8qKkL79u0xdepUHDt2zGN+3mTInX/Hy2P/FlNpnbv/7O3dx/FKnYsKDQ1FaWkpcnNzHd2UWuPt7V2ppIq/vz8KCwsd1CLHcOef/SuvvIK6deviscce48/bg7nz77gp/H2X3P1nb+8+jkGdi3j22WexatUq/fvff/8dXl5eiI6OdmCrald4eDjOnz9vsCw/Px++vr4OalHt8JSf/c8//4yPPvoIK1asgI+Pj8f+vD2Rp/yOV8VTf9896WdfG32cd00bSbWjS5cuePnll9GoUSOUlJTg6aefxoMPPqi/F+8JYmNj8emnn+rfZ2dnQ6PRIDw83IGtsj9P+NkfO3YM99xzDxYuXIj27dsD8NyftyfyhN/x6njq77un/OxrrY+r0TQLqlXTp08XoaGhIjo6WjzzzDPiypUrjm6S3aHcLKni4mJRv359sXTpUiGEEI8//rgYMmSIA1tnP+XPWwj3/tkXFhaKdu3aiUcffVTk5+frH0VFRR7z8yb3/h03hf2b5O4/+9rs4zhRgpxaxQG169evx4QJExAUFAStVoutW7eiQ4cOjm0k1cj69esxcuTISsuPHz+OjIwM/rzJbbF/8wy12ccxqCOXk5OTg71796JXr16oX7++o5tDdsafN3kS/r57Hlv+zBnUEREREbkBzn4lIiIicgMM6oiIiIjcAIM6IiIiIjfAoI6IiIjIDTCoIyIiInIDDOqIiIiI3ACDOiIiIiI3wKCOiIiIyA0wqCMiIiJyA/8PjGwNFWvGypcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-15\n",
    "# 马修斯相关系数计算 k近邻\n",
    "# 1姿势正确，2坐姿偏右，3坐姿偏左，4坐姿前倾\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "# load dataset\n",
    "df = pandas.read_csv('wheelchair_dataset.csv')\n",
    "data = np.array(df)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(100)\n",
    "rng.shuffle(data)\n",
    "data=data.astype(np.float64)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] - 1\n",
    "# 数据最大最小归一化处理\n",
    "maxs = np.amax(data[:,0:d],0)\n",
    "mins = np.amin(data[:,0:d],0)\n",
    "data[:,0:d] = (data[:,0:d] - mins) / (maxs - mins)\n",
    "# 样本长度\n",
    "lens = data.shape[0]\n",
    "# 设置k交叉验证中 k的值\n",
    "k = 4\n",
    "# 测试集长度\n",
    "test_m = lens // k\n",
    "# k近邻分类 中的k值\n",
    "kk = [i for i in range(1,21)]\n",
    "Error = [[],[],[],[]]\n",
    "# 求宏平均F1分数\n",
    "for i in kk:\n",
    "    ta,te,tta,tte = 0.,0.,0.,0.\n",
    "    for j in range(k):\n",
    "        tag_= []\n",
    "        teg_= []\n",
    "        # 区分训练集和测试集\n",
    "        start = j*test_m\n",
    "        ends = (j+1)*test_m if (j != k-1) else lens\n",
    "        if j == 0:\n",
    "            train_x,train_y = data[ends:,0:d],data[ends:,d:]\n",
    "        elif j == k -1:\n",
    "            train_x,train_y = data[0:start,0:d],data[0:start,d:]\n",
    "        else:\n",
    "            train_x,train_y=np.concatenate((data[0:start,0:d],data[ends:,0:d]),0),np.concatenate((data[0:start,d:],data[ends:,d:]),0)\n",
    "        test_x,test_y = data[start:ends,0:d],data[start:ends,d:]\n",
    "        # 计算距离\n",
    "        for t in range(len(test_x)):\n",
    "            _ = np.sum((train_x-test_x[t])*(train_x-test_x[t]),1)# 得到全部的距离\n",
    "            __ = np.argsort(_) # 排序\n",
    "            for h in range(i, len(_)):\n",
    "                if _[__[h]] != _[__[h-1]]:\n",
    "                    break\n",
    "            _ = [train_y[hh] for hh in __[:h]]\n",
    "            teg_.append(int(stats.mode(_,keepdims=False)[0][0]))\n",
    "         # 计算距离\n",
    "        teg_ = np.array(teg_).reshape(1,-1)\n",
    "        for t in range(len(train_x)):\n",
    "            _ = np.sum((train_x-train_x[t])*(train_x-train_x[t]),1)# 得到全部的距离\n",
    "            __ = np.argsort(_)\n",
    "            for h in range(i, len(_)):\n",
    "                if _[__[h]] != _[__[h-1]]:\n",
    "                    break\n",
    "            _ = [train_y[hh] for hh in __[:h]]\n",
    "            tag_.append(int(stats.mode(_,keepdims=False)[0][0]))\n",
    "        # 计算F1分数\n",
    "        # 计算方法就是 测试集和训练集分别一个宏均F1分数，\n",
    "        # 其次采用了k交叉验证，最终的F1分数是这k次计算结果的宏均F1的平均值\n",
    "        tag_ = np.array(tag_).reshape(1,-1)\n",
    "        def G(a,b):\n",
    "            allp = np.sum(b)\n",
    "            c = a*b\n",
    "            TP = np.sum(c)\n",
    "            FP = np.sum(a*(1-c))\n",
    "            if FP + TP == 0:return 0\n",
    "            R = TP/allp\n",
    "            P = TP/(FP+TP)\n",
    "            if not P:return 0\n",
    "            return 2/(1/R + 1/P)\n",
    "        def getF1(li1,li2):\n",
    "            a,b,c,d = li1==4,li1==3,li1==2,li1==1\n",
    "            a1,b1,c1,d1 =  li2==4.,li2==3.,li2==2.,li2==1.\n",
    "            return (G(a,a1)+G(b,b1)+G(c,c1)+G(d,d1))/4\n",
    "        te += getF1(teg_,test_y.transpose())\n",
    "        ta += getF1(tag_,train_y.transpose())\n",
    "        # 计算马修斯相关系数\n",
    "        def getMCC(li1,li2):\n",
    "            a,b,c,d = li1==4,li1==3,li1==2,li1==1\n",
    "            a1,b1,c1,d1 =  li2==4.,li2==3.,li2==2.,li2==1.\n",
    "            A = len(li1[0])\n",
    "            S = np.sum(a*a1)+np.sum(b*b1)+np.sum(c*c1)+np.sum(d*d1)\n",
    "            h = np.array([np.sum(a) , np.sum(b) , np.sum(c) , np.sum(d)])\n",
    "            l = np.array([np.sum(a1) , np.sum(b1),np.sum(c1) , np.sum(d1)])\n",
    "            _ = A*S - np.sum(h*l)\n",
    "            __ = ((A*A - np.sum(h*h))*(A*A-np.sum(l*l)))**0.5\n",
    "            return  _/__\n",
    "        tte += getMCC(teg_,test_y.transpose())\n",
    "        tta += getMCC(tag_,train_y.transpose())\n",
    "            \n",
    "    Error[0].append(ta/k)\n",
    "    Error[1].append(te/k)\n",
    "    Error[2].append(tta/k)\n",
    "    Error[3].append(tte/k)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "axes[0].plot(kk,Error[0],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[0].plot(kk,Error[1],color='b',linestyle='dashed',label='test',marker='o')\n",
    "axes[0].legend()\n",
    "axes[0].set_title('宏平均F1')\n",
    "axes[1].plot(kk,Error[2],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[1].plot(kk,Error[3],color='b',linestyle='dashed',label='test',marker='o')\n",
    "axes[1].legend()\n",
    "axes[1].set_title('MCC')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "362dea46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 浅色显示概率\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 生成 x 值\n",
    "x = np.linspace(0, 10, 100)\n",
    "# 生成对应的 y 值（示例中使用正弦函数）\n",
    "y = np.sin(x)\n",
    "\n",
    "# 生成对应的标准差或不确定性（示例中使用随机生成的值）\n",
    "uncertainty = np.random.uniform(0.1, 10, len(x))\n",
    "\n",
    "# 绘制曲线\n",
    "plt.plot(x, y, label='Curve', color='blue')\n",
    "\n",
    "# 填充曲线两侧的区域表示不确定性或概率范围\n",
    "plt.fill_between(x, y - uncertainty, y + uncertainty, color='lightblue', label='Uncertainty')\n",
    "\n",
    "# 添加标签和标题\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Curve with Uncertainty')\n",
    "plt.legend()\n",
    "\n",
    "# 显示图像\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42d61297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gPv3000C6apxOp5FknE5nywVPP22MdPG/LP9sy7tTX1hu/eX/0OYxHkQY7xjvWG7R5f8QyHgXULAzxpijR4+arVu3mpMnTwbaNGCffPKJ+c1vfnNVA7dfO+HyHXnmjDFPPnlx/sknL843TfX13vVN61uqr6tr/vit1V9ea4wxixe3Xl9b611bW9v88ZrmFy/2rq2ra/n5m+Yv/0NqqvWnvr7+6mpbql+y5FL92bPetVfWX7hwad/++MfNay+vX7Kk5deitfqm/v7kJ77rn3rqUr3b3frfzpkzxjQ0XHr+n/zE99/a+fP+/6011RpzqT+t1Z87d6n2/PnWX68nn/SubWjw/fr+5CfNa/35e2hsbP31vfLYbAXB7hLGu8sw3jHeBfl4F3Cwu1b4vROadqivado0/+snTPB+/Kio1mtvvdW7dsCA1mtvvtm79uabW68dMMC79tZbW6+NivKunTDB9/Zd/oc3bZr/tffc0/Z+bqr/+td914WFXap/6KG2H7epftw433ULFnjX+5qKii79Ldjtvmt37PD/b+3NNy/ts7vu8l27adOl2k2bfNeuWXOp9s03fdf+/OeXanfs8F27YsWl2qIi37W3336ptrTUv9fXB4LdJYx3l2G8u/hfxruLgnC8C+i3Yi1p8WKpjW/cNqsPxD9u6NxtBdq/QLY/kNqwMP/rz53zvz4s7FJ9Robv2rlzL9X/42bZPjX97TQ2+tfvxYulEL+/ryTNmOF/7bVgwgT/6gJ5fREYxrvA6hnvLmG8C0xXjndtRr9rVMDvYJtSc0sfLTedmvCn/srTDU0fubZUf2Xt5acbrqxv6dTE5R/pXl5/ZW1Lpxsur2+ttqX6y99VtHa6wZ9aX/UtnZporb612pbqm04LtDY1nR7x9bfQ0umGtuobGvz/W7v8dEPT6ZHW6i+vvfx0Q0v1V56aaO1v4cralk43XF7vdrdc29r+b3LlqYm2/n5awCd2lzDeXYbxjvEuyMe74A52V57Xbus8N/Wt13envlBv/XpDsLsc410n13envlBv/XpDsDPG8C2xTl3enfrCcusv/weC3SWMd4x3LLfo8n8IZLwL4AS4xTQ2Sk8/3fy8dtP8ldcRUN96fXfqC/XWr0fguttreC3Xd6e+UG/9+qtgM8aYz/wo3ZDL5VJ0dLScTqccDkdXdwdAO+MYv4R9AVhbIMc434oFAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADELRKS0uVnp6u2NhYZWVlyRjjs94YoxUrViglJUXx8fGaP3++amtrPetXrlyphIQEORwO3X333aqqqvKr3c6dOzVkyBDFx8crNze3YzYWQFAg2AEISm63W9OnT9eoUaNUXFyssrIyrV271meb/Px8rVq1SuvXr9euXbtUVFSkuXPnSpIKCwu1bt06FRYWavfu3Tp79qwef/zxNtudOnVKM2bM0OzZs/X+++9r/fr12rFjR4duOwDrItgBCErbtm2T0+lUbm6uBg8erOzsbOXn5/tsU1BQoKysLI0ePVqpqalasmSJXn/9dUlSUVGRpk2bptTUVCUnJ2v27Nnau3dvm+3Wr1+vG264QYsXL1ZKSop+/OMft9kPAGgNwQ5AUCopKdGYMWMUFRUlSRo2bJjKysp8tqmsrFT//v0983a7XXa7XZKUlpamzZs3a//+/Tp58qTy8/M1adKkNtuVlJToS1/6kmw2myRp9OjR2r17t89+uN1uuVwurwkAJIIdgCDlcrk0aNAgz7zNZpPdbld1dXWrbUaMGKGtW7d65tesWaPJkydLkqZOnaqUlBQlJycrISFBtbW1WrhwYZvtruyHw+FQRUWFz74vX75c0dHRnikpKcnv7QZgbQQ7AEEpJCRE4eHhXssiIiJUV1fXapvs7GwVFRUpMzNTw4cP1yuvvKIFCxZIkjZt2qRDhw6pvLxcVVVVSktL0/33399muyv70VYfJGnRokVyOp2e6ciRI1e1DwBYT0hXdwAAukJcXJxKS0u9ltXU1CgsLKzVNgMHDlRZWZnKy8v1xBNPKCEhQZmZmZKkjRs3at68eUpNTZUk5eXlKTo6WqdPn/bZLi4uTqdOnfK7D5IUHh7eLJQCgESwAxCk0tPT9fLLL3vmDx48KLfbrbi4OJ/tbDabHA6Htm/frl27dnmWNzQ06MSJE57548ePS5IaGxt9tktPT9fGjRs983v27FFiYuJn2zgAQYtTsQCC0vjx4+V0OlVQUCBJysnJ0cSJE2W32+VyuXT+/PlW2y5btkz33nuvRo4c6Vk2btw4rV69Wi+++KLWrVunWbNmKSMjQ7179/bZbsaMGXrvvfe0Y8cONTQ06Nlnn9WUKVM6YIsBBAM+sQMQlEJCQrR69WrNmTNHWVlZamxs1M6dOyVd/IZsXl6eZs6c2azdvn37tGHDhmancR955BEdO3ZMS5cuVWVlpTIyMrxuW9Jau/j4eP30pz/VlClTFB0drZ49e3K7EwBXzWbautX6Ncrlcik6OlpOp1MOh6OruwOgnbXXMV5RUaHi4mKNHTtWffr0acceBmbfvn36+OOPNWHChIC3h/EOsLZAjnE+sQMQ1BITE7vFNW3JyclKTk7u6m4AuMZxjR0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCICCnalpaVKT09XbGyssrKyZIzxWW+M0YoVK5SSkqL4+HjNnz9ftbW1nvUrV65UQkKCHA6H7r77blVVVfnVDgAAAM35HezcbremT5+uUaNGqbi4WGVlZVq7dq3PNvn5+Vq1apXWr1+vXbt2qaioSHPnzpUkFRYWat26dSosLNTu3bt19uxZPf744222AwAAQMv8Dnbbtm2T0+lUbm6uBg8erOzsbOXn5/tsU1BQoKysLI0ePVqpqalasmSJXn/9dUlSUVGRpk2bptTUVCUnJ2v27Nnau3dvm+0AAADQMr+DXUlJicaMGaOoqChJ0rBhw1RWVuazTWVlpfr37++Zt9vtstvtkqS0tDRt3rxZ+/fv18mTJ5Wfn69Jkya12Q4AAAAt8zvYuVwuDRo0yDNvs9lkt9tVXV3dapsRI0Zo69atnvk1a9Zo8uTJkqSpU6cqJSVFycnJSkhIUG1trRYuXNhmu9a43W65XC6vCQAAIJj4HexCQkIUHh7utSwiIkJ1dXWttsnOzlZRUZEyMzM1fPhwvfLKK1qwYIEkadOmTTp06JDKy8tVVVWltLQ03X///W22a83y5csVHR3tmZKSkvzdNAAAAEsI8bcwLi5OpaWlXstqamoUFhbWapuBAweqrKxM5eXleuKJJ5SQkKDMzExJ0saNGzVv3jylpqZKkvLy8hQdHa3Tp0/7bNeaRYsW6bHHHvPMu1wuwh0AAAgqfge79PR0vfzyy575gwcPyu12Ky4uzmc7m80mh8Oh7du3a9euXZ7lDQ0NOnHihGf++PHjkqTGxkaf7VoTHh7e7BNFAACAYOJ3sBs/frycTqcKCgr0wAMPKCcnRxMnTpTdbpfL5VJkZKRCQ0NbbLts2TLde++9GjlypGfZuHHjlJubq379+ikyMlJ5eXnKyMhQ7969fbYDAABAy/wOdiEhIVq9erXmzJmjrKwsNTY2aufOnZIufkM2Ly9PM2fObNZu37592rBhQ7PTuI888oiOHTumpUuXqrKyUhkZGV63T2mtHQAAAFpmM239fMQVKioqVFxcrLFjx6pPnz4d1a/PzOVyKTo6Wk6nUw6Ho6u7A6CdcYxfwr4ArC2QY9zvT+yaJCYmKjEx8ao7BwAAgI4R0G/FAgAAoPsi2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdgKBVWlqq9PR0xcbGKisrS8YYn/XGGK1YsUIpKSmKj4/X/PnzVVtb61m/cuVKJSQkyOFw6O6771ZVVZVf66ZPny6bzeaZJk6c2P4bCyAoEOwABCW3263p06dr1KhRKi4uVllZmdauXeuzTX5+vlatWqX169dr165dKioq0ty5cyVJhYWFWrdunQoLC7V7926dPXtWjz/+eJvrJOnDDz/URx99pOrqalVXV+v111/vsO0GYG0EOwBBadu2bXI6ncrNzdXgwYOVnZ2t/Px8n20KCgqUlZWl0aNHKzU1VUuWLPGEsKKiIk2bNk2pqalKTk7W7NmztXfv3jbXHT16VMYYpaWlKSYmRjExMerZs2fHbjwAyyLYAQhKJSUlGjNmjKKioiRJw4YNU1lZmc82lZWV6t+/v2febrfLbrdLktLS0rR582bt379fJ0+eVH5+viZNmtTmuqKiIjU2Nqpfv37q2bOnZs2aperq6o7YZABBgGAHICi5XC4NGjTIM2+z2WS3232GqhEjRmjr1q2e+TVr1mjy5MmSpKlTpyolJUXJyclKSEhQbW2tFi5c2Oa6vXv3atSoUXrrrbdUXFysgwcP6oc//KHPvrvdbrlcLq8JACSCHYAgFRISovDwcK9lERERqqura7VNdna2ioqKlJmZqeHDh+uVV17RggULJEmbNm3SoUOHVF5erqqqKqWlpen+++9vc93ChQu1bds2DR06VEOGDNEzzzyjV1991Wffly9frujoaM+UlJT0WXYFAAsJ6eoOAEBXiIuLU2lpqdeympoahYWFtdpm4MCBKisrU3l5uZ544gklJCQoMzNTkrRx40bNmzdPqampkqS8vDxFR0fr9OnTPtfFxMR4PUdMTIwqKyvldrubBc8mixYt0mOPPeaZd7lchDsAkvjEDkCQSk9P1wcffOCZP3jwoNxut+Li4ny2s9lscjgc2r59u3JycjzLGxoadOLECc/88ePHJUmNjY0+191zzz1e/fjzn/+s66+/vtVQJ0nh4eFyOBxeEwBIfGIHIEiNHz9eTqdTBQUFeuCBB5STk6OJEyfKbrfL5XIpMjJSoaGhLbZdtmyZ7r33Xo0cOdKzbNy4ccrNzVW/fv0UGRmpvLw8ZWRkqHfv3j7XDRs2TI8++qjy8vJ06tQpLV68WA899FBn7QYAVmMsyul0GknG6XR2dVcAdID2OMa3bNliIiMjTd++fU3v3r1NaWmpMcaYAQMGmC1btrTY5pNPPjEOh8McPnzYa3l9fb15+OGHzY033mjCwsLMhAkTzL59+9pcd+7cOfOtb33L9OrVywwePNgsWbLEnD9/PqDtYLwDrC2QY9xmTBu3Wr9GuVwuRUdHy+l0cpoCsKD2OsYrKipUXFyssWPHqk+fPu3Yw87DeAdYWyDHOKdiAQS1xMREJSYmdnU3AKBd8OUJAAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwAAAAsIqBgV1paqvT0dMXGxiorK0vGGJ/1xhitWLFCKSkpio+P1/z581VbW+tZv3LlSiUkJMjhcOjuu+9WVVWVX+sAAADQnN/Bzu12a/r06Ro1apSKi4tVVlamtWvX+myTn5+vVatWaf369dq1a5eKioo0d+5cSVJhYaHWrVunwsJC7d69W2fPntXjjz/e5joAAAC0zO9gt23bNjmdTuXm5mrw4MHKzs5Wfn6+zzYFBQXKysrS6NGjlZqaqiVLluj111+XJBUVFWnatGlKTU1VcnKyZs+erb1797a5DgAAAC3zO9iVlJRozJgxioqKkiQNGzZMZWVlPttUVlaqf//+nnm73S673S5JSktL0+bNm7V//36dPHlS+fn5mjRpUpvrAAAA0DK/g53L5dKgQYM88zabTXa7XdXV1a22GTFihLZu3eqZX7NmjSZPnixJmjp1qlJSUpScnKyEhATV1tZq4cKFba5rjdvtlsvl8poAAACCid/BLiQkROHh4V7LIiIiVFdX12qb7OxsFRUVKTMzU8OHD9crr7yiBQsWSJI2bdqkQ4cOqby8XFVVVUpLS9P999/f5rrWLF++XNHR0Z4pKSnJ300DAACwhBB/C+Pi4lRaWuq1rKamRmFhYa22GThwoMrKylReXq4nnnhCCQkJyszMlCRt3LhR8+bNU2pqqiQpLy9P0dHROn36tM91MTExLT7XokWL9Nhjj3nmXS4X4Q4AAAQVv4Ndenq6Xn75Zc/8wYMH5Xa7FRcX57OdzWaTw+HQ9u3btWvXLs/yhoYGnThxwjN//PhxSVJjY6PPda0JDw9v9okiAABAMPE72I0fP15Op1MFBQV64IEHlJOTo4kTJ8put8vlcikyMlKhoaEttl22bJnuvfdejRw50rNs3Lhxys3NVb9+/RQZGam8vDxlZGSod+/ePtcBAACgZX4Hu5CQEK1evVpz5sxRVlaWGhsbtXPnTkkXvyGbl5enmTNnNmu3b98+bdiwodlp3EceeUTHjh3T0qVLVVlZqYyMDM/tU3ytAwAAQMtspq2fj7hCRUWFiouLNXbsWPXp06ej+vWZuVwuRUdHy+l0yuFwdHV3ALQzjvFL2BeAtQVyjPv9iV2TxMREJSYmXnXnAAAA0DEC+q1YAAAAdF8EOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAglZpaanS09MVGxurrKwsGWN81htjtGLFCqWkpCg+Pl7z589XbW2tZ/3KlSuVkJAgh8Ohu+++W1VVVX6t27lzp4YMGaL4+Hjl5ua2/4YCCBoEOwBBye12a/r06Ro1apSKi4tVVlamtWvX+myTn5+vVatWaf369dq1a5eKioo0d+5cSVJhYaHWrVunwsJC7d69W2fPntXjjz/e5rpTp05pxowZmj17tt5//32tX79eO3bs6NBtB2BdBDsAQWnbtm1yOp3Kzc3V4MGDlZ2drfz8fJ9tCgoKlJWVpdGjRys1NVVLlizR66+/LkkqKirStGnTlJqaquTkZM2ePVt79+5tc9369et1ww03aPHixUpJSdGPf/zjNvsBAK0h2AEISiUlJRozZoyioqIkScOGDVNZWZnPNpWVlerfv79n3m63y263S5LS0tK0efNm7d+/XydPnlR+fr4mTZrU5rqSkhJ96Utfks1mkySNHj1au3fvbvftBRAcCHYAgpLL5dKgQYM88zabTXa7XdXV1a22GTFihLZu3eqZX7NmjSZPnixJmjp1qlJSUpScnKyEhATV1tZq4cKFba67sh8Oh0MVFRU+++52u+VyubwmAJAIdgCCVEhIiMLDw72WRUREqK6urtU22dnZKioqUmZmpoYPH65XXnlFCxYskCRt2rRJhw4dUnl5uaqqqpSWlqb777+/zXVX9qOtPkjS8uXLFR0d7ZmSkpKuah8AsJ6Qru4AAHSFuLg4lZaWei2rqalRWFhYq20GDhyosrIylZeX64knnlBCQoIyMzMlSRs3btS8efOUmpoqScrLy1N0dLROnz7tc11cXJxOnTrldx8kadGiRXrsscc88y6Xi3AHQBLBDkCQSk9P18svv+yZP3jwoNxut+Li4ny2s9lscjgc2r59u3bt2uVZ3tDQoBMnTnjmjx8/LklqbGz0uS49PV0bN270rNuzZ48SExN99iE8PLzZp40AIBHsAASp8ePHy+l0qqCgQA888IBycnI0ceJE2e12uVwuRUZGKjQ0tMW2y5Yt07333quRI0d6lo0bN065ubnq16+fIiMjlZeXp4yMDPXu3dvnuhkzZmj+/PnasWOHMjMz9eyzz2rKlCmdtRsAWI2xKKfTaSQZp9PZ1V0B0AHa4xjfsmWLiYyMNH379jW9e/c2paWlxhhjBgwYYLZs2dJim08++cQ4HA5z+PBhr+X19fXm4YcfNjfeeKMJCwszEyZMMPv27WtznTHGPPfccyY0NNTEx8ebAQMGmE8//TSg7WC8A6wtkGPcZkwbt1q/RrlcLkVHR8vpdMrhcHR1dwC0s/Y6xisqKlRcXKyxY8eqT58+7djDwOzbt08ff/yxJkyYEPD2MN4B1hbIMc6pWABBLTExsc1r2jpDcnKykpOTu7obAK5x3O4EAADAIgh2AAAAFkGwAwAAsAiusQM6QGNjo86fP9/V3bimhYaGen6HFUD3xXj32bXneEewA9qRMUaffvqpTp8+3dVdsYSYmBhdf/31stlsXd0VAFdgvGtf7TXeEeyAdtQ0yPXt21dRUVEEkqtkjFFdXZ1OnjwpSbrhhhu6uEcArsR41z7ae7wj2AHtpLGx0TPI9e7du6u7c82LjIyUJJ08eVJ9+/bltCzQjTDeta/2HO/48gTQTpquMYmKiurinlhH077k+h2ge2G8a3/tNd4R7IB2xumI9sO+BLo3jtH20177kmAHAABgEQQ7AB3i4MGDvJsHEBS603hHsAO6i6eekpYubXnd0qUX13eg22+/XWvXrm23x+vfv7+qq6vb7fEAWEwXjnlWHu8IdkB3YbdLP/5x84Fu6dKLy6+xb4X26NFDMTExXd0NAN2Vhca87jTeEeyAzlBb2/p09uzFmsWLpaefvjigLV58cd3ixRfnn3xSeuwxqb6+7ccN0Ny5c2Wz2bRz505985vflM1m09y5cyVJ3/jGN/TUU0/pl7/8pVJTU/Xzn//c0+4Pf/iDRowYoaioKKWnp6u0tNTrcVs6NfHuu+9q4MCB+s1vfqMBAwYoNjZWq1atCrjPALoxf8Y76eL49uSTrY95P/hB248boKAY74xFOZ1OI8k4nc6u7gqCRH19vSkrKzP19fXNV0qtT9OmedeGhrZeO2GCd218fPOaANXV1Znq6mozbtw489xzz5nq6mpTV1dnjDHm61//uhkzZozJyMgwb775pjly5IgxxpjGxkbTt29fs2zZMnP8+HHzve99z0ydOtXrcQ8cOGCuHGJ27NhhrrvuOpORkWE++ugj87Of/cyEhYV5nu9KvvYpx/gl7At0tnYb76KiGO/+ob3GO25QDHQ3oaFSJ963LTIyUpGRkQoJCVFUVFSz0wl///vftXfvXkVHR3stLykpUXR0tP7617+qpqZGe/fu9ev5zpw5oxdeeEFpaWn63Oc+p+9///s6efKkBgwY0F6bBAAtCobxjmAHdIYzZ1pfd+V1JI89Ji1bJoWFSefOXTwlsXDhxXU9rrh64uDBdu1mSx544IFmg1yPHj2Um5url19+WTfddJMGDBigxsZGvx4vNjZWw4cPlySFhYVJuviTOgAsIpDx7uRJKSen5TGP8e6qEOyAztCzp391S5deHOCefvritSZNFxGHhV2cv9rH9UOPHj1aHHB6tvAc7777rl544QXt27dPCQkJ+t3vfqcPP/zQr+dxOByfua8AurFAxqXcXP/HPMY7vxDsgO6iaUBrGuCkS//98Y+95ztAcnKytm/frqlTp+r//t//qy9+8Yut1p75xztyp9Opffv26bHHHuNTNwCB6cIxz8rjHcEO6C4aG70HuCZN835+9H+1Fi9erNmzZ2vQoEFKSkpSWVlZq7VTp07VjBkzNHLkSA0aNEjf+c53tHDhQp04cUIJCQkd2k8AFtGFY56Vxzub6c6x8zNwuVyKjo6W0+nk1A86xdmzZ3XgwAENGjRIERERXd0dS/C1TznGL2FfoLMx3rW/9hrvuI8dAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCH55AuhmGhulP/xBOn5cuuEGKTOz+e9mA4BVMOa1Lz6xA7qRzZulgQOlL35RmjPn4n8HDry4vKPdfvvtWrt2bbs9ns1m08GDB9vt8QBYT1eNeVYe7wh2QDexebN0zz3S0aPeyysqLi7vjHAHAJ2FMa9jBBTsSktLlZ6ertjYWGVlZamtn5k1xmjFihVKSUlRfHy85s+fr9raWs/6lStXKiEhQQ6HQ3fffbeqqqokSU899ZRsNluz6d133w18C4FuoLa29ens2YunIr7/famlQ6pp2fe/L5050/bjBmru3Lmy2WzauXOnvvnNb8pms2nu3LmSpD//+c+67bbbFB0dra9+9atyOp2edr/85S81cOBA9ezZU1/+8pc9x+/nP/952Ww2SdKgQYNks9n0q1/9KvCOAbgmtTXeSf6PeY2Nvh83UEEx3hk/nT171gwcONA8+OCDZt++fWbatGnmv//7v322eemll0xiYqL505/+ZMrLy82tt95q7r//fmOMMTt37jRDhw415eXl5pNPPjHTpk0zX//6140xxtTX15vq6mrPVFJSYvr06WNOnz7tb3eN0+k0kozT6fS7DfBZ1NfXm7KyMlNfX99s3cWhquVp2jRjduzwXdM0DR/u/bjx8c1rAlVXV2eqq6vNuHHjzHPPPWeqq6s9y3r37m2WLFliDh06ZKZMmWL+7d/+zRhjTE1NjQkJCTEbN240Bw8eNHfeeadZuHChMcYYl8tlqqurjSRTUlJiqqurzblz5wLvmPG9TznGL2FfoLN9lvHOGP/HvB07Lj0u451/x7jfX57Ytm2bnE6ncnNzFRUVpezsbM2fP1/f/OY3W21TUFCgrKwsjR49WpK0ZMkSzZo1S5JUVFSkadOmKTU1VZI0e/ZsPf/885KkiIgIRUREeB7niSee0KOPPqro6OhAMitwzTh+3L+6c+fa/7kjIyMVGRmpkJAQRUVFKSYmRpL02muvKTQ0VIsXL5bNZtOjjz6qr33ta5Iku92u0NBQud1u9e3bV7/5zW88n+D36tXL89gOh8PzeADQxN8xz986fwXDeOd3sCspKdGYMWMUFRUlSRo2bJjKysp8tqmsrFT//v0983a7XfZ/fNUlLS1NCxYs0IMPPqhevXopPz9fkyZNavYYx44d05YtW3TgwAGfz+V2u+V2uz3zLpfL300DOtyVp1AvZ7dLH3zg3+P8x394z3fktboVFRU6deqUYmNjJUkXLlxQTU2Nzp49q8jISP3617/2vMEbN26cnnvuOSUnJ3dchwBcE9oa76SL3371x+V1jHf+8fsaO5fLpUGDBnnmbTab7Ha7qqurW20zYsQIbd261TO/Zs0aTZ48WZI0depUpaSkKDk5WQkJCaqtrdXChQubPcaLL76oOXPm6LrrrvPZv+XLlys6OtozJSUl+btpQIfr2bP1KSLi4tf7+/WT/nGpRjM2m5SUJE2c2PbjXq0ePXp4XTfbr18/3XrrrdqzZ4/27NmjkpIS/eUvf1FoaKiqqqoUGxurXbt26cSJE+rbt68effTRK/psa/M6XADW09Z4J/k/5mVm+n7cq2Xl8c7vYBcSEqLw8HCvZREREaqrq2u1TXZ2toqKipSZmanhw4frlVde0YIFCyRJmzZt0qFDh1ReXq6qqiqlpaXp/vvv92rf2Niol156yXNhoy+LFi2S0+n0TEeOHPF304AuZ7dLP/vZxX9fOdA1zefldey9nZKTk7V9+3YdP35c27dv19SpU3Xo0CEVFRXJbrfrV7/6laZOnSpjjCorK3XHHXfo97//vVwul3r06KELFy40e7zf/va3qqioUGFhYcd1HMA1p6vHPEuPd/5e1JeTk+P54kOT6Ohoc/LkSZ/tLly4YMrKysydd95pJk2a5Fk+c+ZMs2rVKs9804WB1dXVnmVvv/22SUtL87eLXriYGJ3N14Wv/nrtNWP69fO+ODgp6eLyjnb48GEzbtw4Ex4ebpKTk825c+dMUVGRGT16tImKijLp6enmT3/6k6f+ueeeMwMHDjQRERHmtttuM6WlpV6P97//+79m8ODBJiIiwsyZM+eq+sSXJ/zDvkBna4/xzpiuG/OsPN7ZjPHvs8N33nlHDz74oD755BNJ0sGDBzVkyBCdOXPGc91cayoqKpScnKxdu3Zp5MiRkqTp06dr+PDhWrZsmSTpb3/7mz7/+c+rsrJSvXv3liR997vf1fXXX6+nn3464MDqcrkUHR0tp9Mph8MRcHsgUGfPntWBAwc0aNAgry//BIq7sF/ia59yjF/CvkBna6/xTmLMa9Je453fX54YP368nE6nCgoK9MADDygnJ0cTJ06U3W6Xy+VSZGSkQkNDW2y7bNky3XvvvZ5QJ0njxo1Tbm6u+vXrp8jISOXl5SkjI8MT6iTp97//vdatW+dvFwFLsNul22/v6l4AQOdgzGtffge7kJAQrV69WnPmzFFWVpYaGxu1c+dOSRe/IZuXl6eZM2c2a7dv3z5t2LBBpaWlXssfeeQRHTt2TEuXLlVlZaUyMjKUn5/vWb9//34dO3ZM6enpV7lpAAAAwcXvYCdJM2fO1CeffKLi4mKNHTtWffr0kSSfv4+WnJzsdffmJhEREVq1apVWrVrVYrvBgweroaEhkO4BAAAEtYCCnSQlJiYqMTGxI/oCWIKfl63CD+xLoHvjGG0/7bUvA/qtWACta7rG1NctgBCYpn3Z2vW7ALoG4137a6/xLuBP7AC0zG63KyYmRidPnpQkRUVFeX4cGoExxqiurk4nT55UTExMm9+8B9C5GO/aT3uPdwQ7oB1df/31kuQZ7PDZxMTEePYpgO6F8a59tdd4R7AD2pHNZtMNN9ygvn376vz5813dnWtaaGgon9QB3RjjXftpz/GOYAd0ALvdTii5BpSWluqb3/ym9u3bp29/+9tasWKFz9NJxhitXLlSL730kqqrq3XfffdpxYoV6vmPH61cuXKlnn32WdXX12vSpElavXq1evfuraeeekpLlixp9ng7duzQ7bffrunTp+vNN9/0LL/jjju0ffv29t9goAMw3nUvfHkCQFByu92aPn26Ro0apeLiYpWVlWnt2rU+2+Tn52vVqlVav369du3apaKiIs9vWRcWFmrdunUqLCzU7t27dfbsWT3++OOSpIULF6q6utozlZSUqE+fPvqnf/onSdKHH36ojz76yLP+9ddf79BtB2BdBDsAQWnbtm1yOp3Kzc3V4MGDlZ2d7XWT9JYUFBQoKytLo0ePVmpqqpYsWeIJYUVFRZo2bZpSU1OVnJys2bNna+/evZIu3rczJibGM/385z/Xo48+qujoaB09elTGGKWlpXnWN30CCACBItgBCEolJSUaM2aMoqKiJF38BZ2ysjKfbSorK9W/f3/P/OWnoNLS0rR582bt379fJ0+eVH5+viZNmtTsMY4dO6YtW7bo4YcflnQxEDY2Nqpfv37q2bOnZs2aperqap/9cLvdcrlcXhMASAQ7AEHK5XJp0KBBnnmbzSa73e4zVI0YMUJbt271zK9Zs0aTJ0+WJE2dOlUpKSlKTk5WQkKCamtrtXDhwmaP8eKLL2rOnDm67rrrJEl79+7VqFGj9NZbb6m4uFgHDx7UD3/4Q599X758uaKjoz1TUlJSIJsOwMIIdgCCUkhIiMLDw72WRURE+LzhanZ2toqKipSZmanhw4frlVde0YIFCyRJmzZt0qFDh1ReXq6qqiqlpaXp/vvv92rf2Niol156yXNdnnTx+rtt27Zp6NChGjJkiJ555hm9+uqrPvu+aNEiOZ1Oz3TkyJFANx+ARfGtWABBKS4uTqWlpV7LampqFBYW1mqbgQMHqqysTOXl5XriiSeUkJCgzMxMSdLGjRs1b948paamSpLy8vIUHR2t06dPKyYmRtLFb8HGx8dryJAhrT5HTEyMKisr5Xa7mwXPJuHh4a2uAxDc+MQOQFBKT0/XBx984Jk/ePCg3G634uLifLaz2WxyOBzavn27cnJyPMsbGhp04sQJz/zx48clXfyUrsmmTZt01113eT3ePffc49WPP//5z7r++usJbgCuCsEOQFAaP368nE6nCgoKJEk5OTmaOHGi7Ha7XC6XzxuuLlu2TPfee69GjhzpWTZu3DitXr1aL774otatW6dZs2YpIyNDvXv39tT8/ve/1xe/+EWvxxo2bJgeffRR/elPf9Kbb76pxYsX66GHHmrnrQUQLDgVCyAohYSEaPXq1ZozZ46ysrLU2NionTt3SroYtvLy8jRz5sxm7fbt26cNGzY0O437yCOP6NixY1q6dKkqKyuVkZHhdfuU/fv369ixY0pPT/dqt2jRIh06dEiTJk1S3759NW/ePC1atKj9NxhAULAZY0xXd6IjuFwuRUdHy+l0yuFwdHV3ALSz9jrGKyoqVFxcrLFjx6pPnz7t2MPOw3gHWFsgxzif2AEIaomJiUpMTOzqbgBAu+AaOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEBBbvS0lKlp6crNjZWWVlZMsb4rDfGaMWKFUpJSVF8fLzmz5+v2tpaz/qVK1cqISFBDodDd999t6qqqpo9xqxZs/Twww8H0k0AAICg5Hewc7vdmj59ukaNGqXi4mKVlZVp7dq1Ptvk5+dr1apVWr9+vXbt2qWioiLNnTtXklRYWKh169apsLBQu3fv1tmzZ/X44497tX/rrbf0zjvvaOnSpYFvGQAAQJDxO9ht27ZNTqdTubm5Gjx4sLKzs5Wfn++zTUFBgbKysjR69GilpqZqyZIlev311yVJRUVFmjZtmlJTU5WcnKzZs2dr7969nrb19fV66KGHlJOTo5iYmKvbOgAAgCDid7ArKSnRmDFjFBUVJUkaNmyYysrKfLaprKxU//79PfN2u112u12SlJaWps2bN2v//v06efKk8vPzNWnSJE/t0qVLVV9fr5CQEL3zzjttnvZ1u91yuVxeEwAAQDDxO9i5XC4NGjTIM2+z2WS321VdXd1qmxEjRmjr1q2e+TVr1mjy5MmSpKlTpyolJUXJyclKSEhQbW2tFi5cKEk6fPiwcnNzlZycrMOHDysrK0tf/epXfYa75cuXKzo62jMlJSX5u2kAAACW4HewCwkJUXh4uNeyiIgI1dXVtdomOztbRUVFyszM1PDhw/XKK69owYIFkqRNmzbp0KFDKi8vV1VVldLS0nT//fdLktauXauEhAS9/fbbevLJJ/Xuu+9q586devvtt1t9rkWLFsnpdHqmI0eO+LtpAAAAlhDib2FcXJxKS0u9ltXU1CgsLKzVNgMHDlRZWZnKy8v1xBNPKCEhQZmZmZKkjRs3at68eUpNTZUk5eXlKTo6WqdPn9bRo0d1xx13eIJkr169lJKSogMHDrT6XOHh4c2CJwAAQDDx+xO79PR0ffDBB575gwcPyu12Ky4uzmc7m80mh8Oh7du3Kycnx7O8oaFBJ06c8MwfP35cktTY2KikpCTV19d71l24cEFHjx7VgAED/O0uALSps27h9NRTT8lmszWb3n33XUnSzp07NWTIEMXHxys3N7fDtheA9fkd7MaPHy+n06mCggJJUk5OjiZOnCi73S6Xy6Xz58+32nbZsmW69957NXLkSM+ycePGafXq1XrxxRe1bt06zZo1SxkZGerdu7f+5V/+RW+88YZee+01HT16VIsWLZLb7da4ceM+w6YCwCWdeQunhQsXqrq62jOVlJSoT58++qd/+iedOnVKM2bM0OzZs/X+++9r/fr12rFjR0dvPgCrMgHYsmWLiYyMNH379jW9e/c2paWlxhhjBgwYYLZs2dJim08++cQ4HA5z+PBhr+X19fXm4YcfNjfeeKMJCwszEyZMMPv27fOsf/PNN82IESNMRESEGTp0qHnvvfcC6apxOp1GknE6nQG1A3Bt+KzH+JYtW0xsbKypra01xhizZ88eM27cOJ9tMjMzTV5enmf+t7/9renVq5cxxpiVK1earKwsz7pf/OIXJiMjo8XH+c53vmOys7ONMcb8x3/8h0lNTTUXLlwwxhizdetW86//+q8BbQvjHWBtgRzjfl9jJ0kzZ87UJ598ouLiYo0dO1Z9+vSRdPG0bGuSk5PldDqbLY+IiNCqVau0atWqFtt95Stf0Ve+8pVAugcAfuuIWzgtWLBADz74oHr16tXsFk5Njh07pi1btniuGS4pKdGXvvQl2Ww2SdLo0aO1aNEin/1wu91yu92eeW7vBKBJwL8Vm5iYqH/+53/2hDoAuBZ15i2cLvfiiy9qzpw5uu6661rsh8PhUEVFhc++c3snAK0JONgBgBV05i2cmjQ2Nuqll17yXJfXUj/a6oPE7Z0AtC6gU7EAYBWdeQunpp9F3LFjh+Lj4zVkyBCvfpw6dcrvPkjc3glA6/jEDkBQ6sxbODXZtGmT7rrrLp/92LNnjxITE69uowAEPYIdgKDUmbdwavL73/9eX/ziF70ea8aMGXrvvfe0Y8cONTQ06Nlnn9WUKVPaeWsBBAtOxQIISiEhIVq9erXmzJmjrKwsNTY2aufOnZIufkM2Ly9PM2fObNZu37592rBhQ7PTuI888oiOHTumpUuXqrKyUhkZGcrPz/es379/v44dO6b09HSvdvHx8frpT3+qKVOmKDo6Wj179vRqBwCBsBnTxq3Wr1Eul0vR0dFyOp1yOBxd3R0A7ay9jvGKiopmt3DqCvv27dPHH3+sCRMmBLw9jHeAtQVyjPOJHYCglpiY2C2uaUtOTlZycnJXdwPANY5r7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHYCgVVpaqvT0dMXGxiorK0vGGJ/1xhitWLFCKSkpio+P1/z581VbW+tZv3LlSiUkJMjhcOjuu+9WVVVVs8eYNWuWHn74Ya9l06dPl81m80wTJ05snw0EEHQIdgCCktvt1vTp0zVq1CgVFxerrKxMa9eu9dkmPz9fq1at0vr167Vr1y4VFRVp7ty5kqTCwkKtW7dOhYWF2r17t86ePavHH3/cq/1bb72ld955R0uXLvVa/uGHH+qjjz5SdXW1qqur9frrr7frtgIIHgEFu858d8s7WAAdadu2bXI6ncrNzdXgwYOVnZ2t/Px8n20KCgqUlZWl0aNHKzU1VUuWLPGEsKKiIk2bNk2pqalKTk7W7NmztXfvXk/b+vp6PfTQQ8rJyVFMTIxn+dGjR2WMUVpammJiYhQTE6OePXt2yDYDsD6/g11nv7vlHSyAjlRSUqIxY8YoKipKkjRs2DCVlZX5bFNZWan+/ft75u12u+x2uyQpLS1Nmzdv1v79+3Xy5Enl5+dr0qRJntqlS5eqvr5eISEheueddzxvjIuKitTY2Kh+/fqpZ8+emjVrlqqrq332w+12y+VyeU0AIAUQ7Drz3S3vYAF0NJfLpUGDBnnmbTab7Ha7z1A1YsQIbd261TO/Zs0aTZ48WZI0depUpaSkKDk5WQkJCaqtrdXChQslSYcPH1Zubq6Sk5N1+PBhZWVl6atf/aqMMdq7d69GjRqlt956S8XFxTp48KB++MMf+uz78uXLFR0d7ZmSkpI+w54AYCV+B7vOfHfLO1gAHS0kJETh4eFeyyIiIlRXV9dqm+zsbBUVFSkzM1PDhw/XK6+8ogULFkiSNm3apEOHDqm8vFxVVVVKS0vT/fffL0lau3atEhIS9Pbbb+vJJ5/Uu+++q507d+rtt9/WwoULtW3bNg0dOlRDhgzRM888o1dffdVn3xctWiSn0+mZjhw58hn3BgCr8DvYdea7W97BAuhocXFxOnXqlNeympoahYWFtdpm4MCBKisr0+rVq9W/f39NmjRJmZmZkqSNGzdq3rx5Sk1NVVxcnPLy8rR582adPn1aR48e1R133OEJkr169VJKSooOHDjQ7DliYmJUWVkpt9vdaj/Cw8PlcDi8JgCQAgh2nfnulnewADpaenq6PvjgA8/8wYMH5Xa7FRcX57OdzWaTw+HQ9u3blZOT41ne0NCgEydOeOaPHz8uSWpsbFRSUpLq6+s96y5cuKCjR49qwIABuueee7z68ec//1nXX399s/EWAPwR4m9hXFycSktLvZb5++62vLxcTzzxhBISElp8dytJeXl5io6O1unTp72+MSZ5v4NtbbALDw9nIATgt/Hjx8vpdKqgoEAPPPCAcnJyNHHiRNntdrlcLkVGRio0NLTFtsuWLdO9996rkSNHepaNGzdOubm56tevnyIjI5WXl6eMjAz17t1b//Iv/6JRo0bptdde02233ab//M//lNvt1rhx41RUVKRHH31UeXl5OnXqlBYvXqyHHnqos3YDAKsxfvrf//1fk5yc7Jk/cOCAiYiIMA0NDW22PXr0qImIiDAffvihZ9mdd95pfvSjH3nmy8vLjSRTWVlp7r77bvP+++971r300kvm+uuv97erxhhjnE6nkWScTmdA7QBcG9rjGN+yZYuJjIw0ffv2Nb179zalpaXGGGMGDBhgtmzZ0mKbTz75xDgcDnP48GGv5fX19ebhhx82N954owkLCzMTJkww+/bt86x/8803zYgRI0xERIQZOnSoee+994wxxpw7d85861vfMr169TKDBw82S5YsMefPnw9oOxjvAGsL5Bi3GdPGzej+oaGhQTfeeKOeffZZPfDAA5o7d64qKir0xhtvtPnudt68eaqtrVVBQYFnWU5OjnJzc/X000973t1GRkbqj3/8o55++mlt27bN8w72O9/5jh566CEtXrzY78DqcrkUHR0tp9PJ9SeABbXXMV5RUaHi4mKNHTtWffr0accedh7GO8DaAjnG/T4VGxISotWrV2vOnDnKyspSY2Ojdu7cKeniN2Tz8vI0c+bMZu327dunDRs2NDuN+8gjj+jYsWNaunSpKisrlZGR4bl9yqJFi3To0CFNmjRJffv21bx587Ro0SJ/uwoAfktMTFRiYmJXdwMA2oXfn9g1uVbe3fIOFrA2jvFL2BeAtXXIJ3ZNeHcLAADQPQX0W7EAAADovgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIkK6ugNdrbFR+sMfpOPHpRtukDIzJbud+qup7059od769Qhcd3sNr+X67tQX6q1fHxBjUU6n00gyTqez1ZrXXjOmXz9jpEtTv34Xl1MfWH136gv11q83xr9jPFgw3nVufXfqC/XWrzcmsPEuaIPda68ZY7N571jp4jKbrfkOpr71+u7UF+qtX9+EYHcJ413n1XenvlBv/fomBDvjeyc0NDRPy1fu4H79jHE6jTlz5uLkb31dnf+PX1MTWH8aGi7VJyb6rk9KulRfU+N/fV3dxe3wp/7MmcBq/a2/fN9boT6Qv7WzZ/2vP3/e/7+d+vqr+1tzu/3/22lo8P/v4dy5wP5+mvrj7zEebBjvGO+6Sz3jXdePdwp0ALlW+NoJO3a0vmNbmm67zf/aCRP8f/zPfS6w/uzYcXX1n/uc//UTJnTMfhk/PrB9HqxTdrb/tZs2+f+38O//fnV/O//xH/7XFxX53/f//u/A9ktTf/w9xoMN4x3j3bU4Md757o+/x/iVgvJbscePB1bvdnfM458/H1h9U12g9U3P42+9vwLZL2fPBvbYwaq6OrB6f1+z06cDq2+qq6pq3340qawMrD7Qx8cljHe+6/3FeNf+GO/a5/GvFJTB7oYb/Kv73e+kM2ekZ57xv37bNv8f/4UXAutPU12g9U3P40/9tm0Xt8Mfzzzjf+3SpRf3pb/1Tfs+2OqnTPG//q67/P9buOeei/8N9G/n9tv9rx850v/tHTUqsP3jb7/RHONd6/WMd11bz3jnuz9Xrc3P9K5R/lxz0tIFjFLz89zUt17fnfpCvfXr/T3Ggw3jHeMd9dar9/cYv5LarLhG+fstsSt3cFvfZKG+eX136gv11q9vQrC7hPGu8+q7U1+ot359E4Kdufr7OiUltb5jqW+9vjv1hXrr1xtDsLsc413n1nenvlBv/XpjAhvvbMYY8xnP5nZLLpdL0dHRcjqdcjgcrdZ1t7tLX8v13akv1Fu/3t9jPBgw3nV+fXfqC/XWrw9kvAv6YAfg2sQxfgn7ArC2QI7xoPxWLAAAgBUR7AAAACyCYAcAAGARBDsAAACLINgBAABYBMEOAADAIgh2AAAAFkGwAwAAsAiCHQAAgEUQ7AAAACwipKs70FGafinN5XJ1cU8AdISmY9uiv4oYEMY7wNoCGe8sG+xqamokSUlJSV3cEwAdqaamRtHR0V3djS7FeAcEB3/GO5ux6NvdCxcu6NixY+rVq5dsNltXd6dDuVwuJSUl6ciRI5b/AfBg2laJ7fXFGKOamhrdeOON6tEjuK8qYbyzpmDaVont9SWQ8c6yn9j16NFD/fr16+pudCqHwxEUB4MUXNsqsb2tCfZP6pow3llbMG2rxPa2xt/xLrjf5gIAAFgIwQ4AAMAiCHYWEB4erp/85CcKDw/v6q50uGDaVontBa4UTH8jwbStEtvbXiz75QkAAIBgwyd2AAAAFkGwAwAAsAiCHQAAgEUQ7K5hDz/8sGw2m2dKTk7u6i7hM6qqqtKgQYN08OBBzzJeZ4DjwIoY7zoGwe4a9uGHH+q3v/2tqqurVV1drb/85S9d3aUO0dLBX1paqvT0dMXGxiorK8sSvxdaWVmpO++802s7Jeu+zq+//rpuuukmhYSE6LbbbtPHH38syZqvLT47qx4HV2K8s+br3JnjHcHuGtXQ0KDS0lKNHz9eMTExiomJUa9evbq6W+2upYPf7XZr+vTpGjVqlIqLi1VWVqa1a9d2WR/by6xZszRr1iyvZVZ9nffv369vfvObysnJUUVFhQYMGKBvf/vbln1t8dlY9Ti4EuOdNV/nTh/vDK5JH374obnuuuvM4MGDTUREhJkyZYo5dOhQV3er3d1xxx0mLy/PSDIHDhwwxhizZcsWExsba2pra40xxuzZs8eMGzeuC3vZPvbv32+MMV7batXX+Y033jAvvPCCZ/6dd94xYWFhln1t8dlY9Ti4EuOdNV/nzh7v+MTuGvXxxx9r6NCh2rhxo8rKyhQaGqoHH3ywq7vV7lavXq3vf//7XstKSko0ZswYRUVFSZKGDRumsrKyruheu7rpppuaLbPq63znnXdq7ty5nvm//e1vSk5Otuxri8/GqsfBlRjvrPk6d/Z4xw2KLeLQoUO66aabVF1dbckfT7bZbDpw4IAGDhyoxx9/XGfPntVzzz3nWd+nTx/t3btXsbGxXdjL9nH5tl7Jiq/zuXPndPPNN+vRRx/V3//+d0u/tmgfVjwOLsd4d5EVX+fOGO/4xM4iYmJidOHCBR0/fryru9LhQkJCmv0ES0REhOrq6rqoR53Hiq/zk08+qeuuu07f/e53g/q1hf+seBy0JpiPCSu+zp0x3hHsrlGPPfaYNm3a5Jn/85//rB49eigpKakLe9U54uLidOrUKa9lNTU1CgsL66IedRyrv85vv/22XnzxRW3YsEGhoaFB9drCf1Y/DnwJpmPC6q9zZ413IZ+pNbrMiBEj9KMf/UjXX3+9Ghoa9PDDD+sb3/iG51y9laWnp+vll1/2zB88eFBut1txcXFd2KuOYeXX+e9//7v+9V//VS+88IJuvvlmScH12sJ/Vj4O2hJMx4SVX+dOHe8+89cv0GUWLlxoYmJiTFJSkvne975nzpw509Vd6jC67JtT58+fN3369DHr1q0zxhjz4IMPmjvvvLMLe9e+Lt9WY6z5OtfV1ZkhQ4aY73znO6ampsYznTt3ztKvLa6eFY+D1jDeWet17uzxji9P4Jpw5QW2W7du1Zw5c9SrVy81NjZq586dGjp0aNd2En7bunWr7rrrrmbLDxw4oD179vDaIqgx3llLZ493BDtcsyoqKlRcXKyxY8eqT58+Xd0dtCNeW8Abx4R1tfdrS7ADAACwCL4VCwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBH/H83h1Wl/sH9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 高斯朴素贝叶斯分类器 2-16\n",
    "# 1姿势正确，2坐姿偏右，3坐姿偏左，4坐姿前倾\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "# load dataset\n",
    "df = pandas.read_csv('wheelchair_dataset.csv')\n",
    "data = np.array(df)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "data=data.astype(np.float64)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] - 1\n",
    "# 样本长度\n",
    "lens = data.shape[0]\n",
    "# 设置k交叉验证中 k的值\n",
    "k = 4\n",
    "# 测试集长度\n",
    "test_m = lens // k\n",
    "# k近邻分类 中的k值\n",
    "kk = [i for i in range(1,21)]\n",
    "Error = [[],[],[],[]]\n",
    "# 求宏平均F1分数\n",
    "for i in kk:\n",
    "    ta,te,tta,tte = 0.,0.,0.,0.\n",
    "    for j in range(k):\n",
    "        tag_= []\n",
    "        teg_= []\n",
    "        # 区分训练集和测试集\n",
    "        start = j*test_m\n",
    "        ends = (j+1)*test_m if (j != k-1) else lens\n",
    "        if j == 0:\n",
    "            train_x,train_y = data[ends:,0:d],data[ends:,d:]\n",
    "        elif j == k -1:\n",
    "            train_x,train_y = data[0:start,0:d],data[0:start,d:]\n",
    "        else:\n",
    "            train_x,train_y=np.concatenate((data[0:start,0:d],data[ends:,0:d]),0),np.concatenate((data[0:start,d:],data[ends:,d:]),0)\n",
    "        test_x,test_y = data[start:ends,0:d],data[start:ends,d:]\n",
    "        # 高斯朴素贝叶斯\n",
    "        # 计算ujk 和 pck\n",
    "        def getUjkAndPck(lia,lib):\n",
    "            ujk = np.array([[0. for _ in range(4)] for __ in range(d)]) # d *4 的矩阵，输入特征维度*类别总数\n",
    "            times = [0 for _  in range (4)]\n",
    "            for tt in range(len(lib)):\n",
    "                kk = int(lib[tt])-1 # 得到的是 类别\n",
    "                ujk[:,kk] += lia[tt]  #  注意横纵坐标的含义\n",
    "                times[kk] += 1\n",
    "            for _ in range(d):\n",
    "                ujk[_] /= times \n",
    "            return ujk,np.array(times)/len(lia)\n",
    "        # 方差的无偏估计量\n",
    "        def getOjk2(lia,lib,ujk):\n",
    "            Ojk2 = np.array([[0. for _ in range(4)] for __ in range(d)])\n",
    "            times  = [0 for i in range(4)]\n",
    "            for _ in range(len(lib)):\n",
    "                kk = int(lib[_])-1\n",
    "                Ojk2[:,kk] += (lia[_] - ujk[:,kk])**2 # 注意横纵坐标的含义\n",
    "                times[kk] += 1\n",
    "            times = np.array(times)-1\n",
    "            for _ in range(d):\n",
    "                Ojk2[_] /= times\n",
    "            return Ojk2**0.5\n",
    "        ujk,pck = getUjkAndPck(train_x,train_y.transpose()[0])\n",
    "        Ojk2 = getOjk2(train_x,train_y.transpose()[0],ujk)\n",
    "        # 测试集\n",
    "        for _ in test_x:\n",
    "            __ = (ujk.transpose() - _)/Ojk2.transpose()\n",
    "            __ = __**2\n",
    "            __ = np.log(pck)-0.5*np.sum(__,1) -np.sum(np.log(Ojk2).transpose(),1)\n",
    "            teg_.append(np.argmax(__)+1)\n",
    "        teg_ = np.array(teg_).reshape(1,-1)\n",
    "        # 训练集\n",
    "        for _ in train_x:\n",
    "            __ = (ujk.transpose() - _)/Ojk2.transpose()\n",
    "            __ = __**2\n",
    "            __ = np.log(pck)-0.5*np.sum(__,1) -np.sum(np.log(Ojk2).transpose(),1)\n",
    "            tag_.append(np.argmax(__)+1)\n",
    "        tag_ = np.array(tag_).reshape(1,-1)\n",
    "                            \n",
    "                        \n",
    "\n",
    "        \n",
    "        # 计算F1分数\n",
    "        # 计算方法就是 测试集和训练集分别一个宏均F1分数，\n",
    "        # 其次采用了k交叉验证，最终的F1分数是这k次计算结果的宏均F1的平均值\n",
    "        \n",
    "        def G(a,b):\n",
    "            allp = np.sum(b)\n",
    "            c = a*b\n",
    "            TP = np.sum(c)\n",
    "            FP = np.sum(a*(1-c))\n",
    "            if FP + TP == 0:return 0\n",
    "            R = TP/allp\n",
    "            P = TP/(FP+TP)\n",
    "            if not P:return 0\n",
    "            return 2/(1/R + 1/P)\n",
    "        def getF1(li1,li2):\n",
    "            a,b,c,d = li1==4,li1==3,li1==2,li1==1\n",
    "            a1,b1,c1,d1 =  li2==4.,li2==3.,li2==2.,li2==1.\n",
    "            return (G(a,a1)+G(b,b1)+G(c,c1)+G(d,d1))/4\n",
    "        te += getF1(teg_,test_y.transpose())\n",
    "        ta += getF1(tag_,train_y.transpose())\n",
    "        # 计算马修斯相关系数\n",
    "        def getMCC(li1,li2):\n",
    "            a,b,c,d = li1==4,li1==3,li1==2,li1==1\n",
    "            a1,b1,c1,d1 =  li2==4.,li2==3.,li2==2.,li2==1.\n",
    "            A = len(li1[0])\n",
    "            S = np.sum(a*a1)+np.sum(b*b1)+np.sum(c*c1)+np.sum(d*d1)\n",
    "            h = np.array([np.sum(a) , np.sum(b) , np.sum(c) , np.sum(d)])\n",
    "            l = np.array([np.sum(a1) , np.sum(b1),np.sum(c1) , np.sum(d1)])\n",
    "            _ = A*S - np.sum(h*l)\n",
    "            __ = ((A*A - np.sum(h*h))*(A*A-np.sum(l*l)))**0.5\n",
    "            return  _/__\n",
    "        tte += getMCC(teg_,test_y.transpose())\n",
    "        tta += getMCC(tag_,train_y.transpose())\n",
    "            \n",
    "    Error[0].append(ta/k)\n",
    "    Error[1].append(te/k)\n",
    "    Error[2].append(tta/k)\n",
    "    Error[3].append(tte/k)\n",
    "# 绘图部分\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "axes[0].plot(kk,Error[0],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[0].plot(kk,Error[1],color='b',linestyle='dashed',label='test',marker='o')\n",
    "axes[0].legend()\n",
    "axes[0].set_title('宏平均F1')\n",
    "axes[1].plot(kk,Error[2],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[1].plot(kk,Error[3],color='b',linestyle='dashed',label='test',marker='o')\n",
    "axes[1].legend()\n",
    "axes[1].set_title('MCC')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d60d9261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0.004653165783844703\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多分类逻辑回归 2-17\n",
    "# 1姿势正确，2坐姿偏右，3坐姿偏左，4坐姿前倾\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import random as rd\n",
    "# load dataset\n",
    "df = pandas.read_csv('wheelchair_dataset.csv')\n",
    "data = np.array(df)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "data=data.astype(np.float64)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] - 1\n",
    "# 样本长度\n",
    "lens = data.shape[0]\n",
    "# 设置训练次数\n",
    "epochs = 5000\n",
    "# 设置学习率\n",
    "xuexi = 0.1\n",
    "# 标准化处理\n",
    "mean  = np.mean(data[:,:d],0)\n",
    "stds = np.std(data[:,:d],0,ddof=1)\n",
    "data[:,:d] = (data[:,:d]-mean)/stds\n",
    "# 多分类逻辑回归\n",
    "# 初始化 w，b\n",
    "w = np.array([[rd.uniform(0,1) for __ in range(d)] for _ in range(4)]) # c * d\n",
    "b = np.array([[rd.uniform(0,1) ] for _ in range(4)]) # c*1\n",
    "# y-> one-hot\n",
    "datay = np.array([[i==1.,i==2.,i==3.,i==4.] for i in data[:,d]])\n",
    "# 划分数据集和测试集\n",
    "train_m = int(lens*0.8)\n",
    "test_m = lens - train_m\n",
    "train_x,train_y,test_x,test_y = data[:train_m,:d],datay[:train_m],data[train_m:,:d],datay[train_m:]#  x:m*d,y:m*c\n",
    "Errors = [[],[],[],[]]\n",
    "for _ in range(epochs):\n",
    "    # 更新w,b\n",
    "    # 第一步计算 Z\n",
    "    Z = np.dot(w,train_x.transpose()) + np.dot(b,np.ones(train_m).reshape(1,-1))\n",
    "    # 计算 Y^\n",
    "    _ = np.random.rand(4,4)>0 # c*c矩阵\n",
    "    Y_ = np.exp(Z)/np.dot(_,np.exp(Z)) # c*m\n",
    "    # 计算 E\n",
    "    E = Y_ - train_y.transpose() # c*m\n",
    "    # 计算多类别逻辑回归交叉熵\n",
    "    TaJwb = np.trace(np.dot(train_y,np.log(Y_)))/-train_m\n",
    "    Errors[2].append(TaJwb)\n",
    "    # 统计训练集出错\n",
    "    _ = np.argmax(Y_,0) + 1\n",
    "    _ = _.astype(np.float64)\n",
    "    _ -= data[:train_m,d]\n",
    "    taer = np.sum(_ > 0)\n",
    "    taer += np.sum(_ < 0)\n",
    "    Errors[0].append(taer)\n",
    "    # 统计测试集出错\n",
    "    # 第一步计算 Z\n",
    "    Z = np.dot(w,test_x.transpose()) + np.dot(b,np.ones(test_m).reshape(1,-1))\n",
    "    # 计算 Y^\n",
    "    _ = np.random.rand(4,4)>0 # c*c矩阵\n",
    "    Y_ = np.exp(Z)/np.dot(_,np.exp(Z)) # c*m\n",
    "    _ = np.argmax(Y_,0) + 1\n",
    "    _ = _.astype(np.float64)\n",
    "    _ -= data[train_m:,d]\n",
    "    teer = np.sum(_ > 0)\n",
    "    teer += np.sum(_ < 0)\n",
    "    Errors[1].append(teer)\n",
    "    # 更新w,b\n",
    "    b -= np.dot(E,np.ones(train_m).reshape(-1,1))/train_m\n",
    "    w -= np.dot(E,train_x)/train_m\n",
    "# 绘图部分\n",
    "print(Errors[0][-1],Errors[1][-1],Errors[2][-1])\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "kk = [i for i in range(epochs)]\n",
    "axes[0].plot(kk,Errors[0],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[0].plot(kk,Errors[1],color='b',linestyle='dashed',label='test',marker='x')\n",
    "axes[0].legend()\n",
    "axes[0].set_title('trainErrors')\n",
    "axes[1].plot(kk,Errors[2],color='r',linestyle='dashed',label='train',marker='x')\n",
    "axes[1].legend()\n",
    "axes[1].set_title('交叉熵')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "96c37575",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.22008789 0.24716262 0.2763877  0.3755695  0.41167509 0.4487572\n",
      "  0.56177312 0.56921071 0.57661721 0.58398943 0.59132427 0.59861867\n",
      "  0.60586965 0.6130743  0.62022981 0.62733343 0.64137447 0.64830684\n",
      "  0.65517726 0.66198344 0.6687232  0.67539448 0.6819953  0.68852379\n",
      "  0.69497819 0.70135687 0.70765826 0.71388093 0.72002355]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 二分类逻辑回归对其进行预测\n",
    "import numpy as np\n",
    "import random as rd\n",
    "x_train = np.array([0, 5, 10, 25, 30, 35, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]).reshape((1, -1))\n",
    "y_train = np.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape((1, -1))\n",
    "m_train = x_train.size # number of training examples\n",
    "# 第一步随机排序  （这里感觉不用）\n",
    "epoch = 30000 # 训练次数\n",
    "xuexi = 0.0001 # 学习率\n",
    "# 设置权重和偏差\n",
    "w = 0.\n",
    "b = 0.\n",
    "for _ in range(epoch):\n",
    "    # 第一步计算e\n",
    "    Y_ = 1/(1+np.exp(-(w*x_train + b)))\n",
    "    e = Y_ - y_train\n",
    "    # 更新 w,b\n",
    "    b -= xuexi*np.dot(e,np.ones(m_train).transpose())/m_train\n",
    "    w -= xuexi*np.dot(x_train,e.transpose())/m_train\n",
    "# 绘图部分\n",
    "Y =  1/(1+np.exp(-(w*x_train + b)))\n",
    "print(Y)\n",
    "# lines = 60*w + b\n",
    "Y_= Y >= 0.5\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "kk = [i for i in range(epochs)]\n",
    "plt.plot(x_train[0],Y[0],color='k',linestyle='dashed',label='train')\n",
    "plt.plot(x_train[0],Y_[0],color='r',linestyle='dashed',label='predict')\n",
    "plt.scatter(x_train,y_train[0],color='b',label='原本',marker='x')\n",
    "plt.legend()\n",
    "plt.title('变化')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30c8a0d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 二分类逻辑回归对其进行预测\n",
    "# 强化\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import random as rd\n",
    "x_train = np.array([0, 5, 10, 25, 30, 35, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]).reshape((1, -1))\n",
    "y_train = np.array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape((1, -1))\n",
    "m_train = x_train.size # number of training examples\n",
    "# 第一步随机排序  （这里感觉不用）\n",
    "epoch = 30000 # 训练次数\n",
    "xuexi = 0.001 # 学习率\n",
    "# 设置权重和偏差\n",
    "# 标准化\n",
    "x_train=x_train.astype(np.float64)\n",
    "mean = np.mean(x_train,1)\n",
    "stds = np.std(x_train,1,ddof=1)\n",
    "x_train = (x_train - mean)/stds\n",
    "w0,w1,w2,w3 = 0.,0.,0.,0.\n",
    "b0,b1,b2,b3 = 0.,0.,0.,0.\n",
    "for _ in range(epoch):\n",
    "    # 第一步计算e0\n",
    "    Y_ = 1/(1+np.exp(-(w0*x_train + b0)))\n",
    "    e = Y_ - y_train\n",
    "    # 更新 w0,b0\n",
    "    b0 -= xuexi*np.dot(e,np.ones(m_train).transpose())/m_train\n",
    "    w0 -= xuexi*np.dot(x_train,e.transpose())/m_train\n",
    "    # 第一步计算e1\n",
    "    Y_ = 1/(1+np.exp(-(w1*x_train + b1)+17.5))\n",
    "    e = Y_ - y_train\n",
    "    # 更新 w,b\n",
    "    b1 -= xuexi*np.dot(e,np.ones(m_train).transpose())/m_train\n",
    "    w1 -= xuexi*np.dot(x_train,e.transpose())/m_train\n",
    "    # 第一步计算e2\n",
    "    Y_ = 1/(1+np.exp(-(w2*x_train + b2)  - 42.5))\n",
    "    e = Y_ - y_train\n",
    "    # 更新 w,b\n",
    "    b2 -= xuexi*np.dot(e,np.ones(m_train).transpose())/m_train\n",
    "    w2 -= xuexi*np.dot(x_train,e.transpose())/m_train\n",
    "    # 第一步计算e3\n",
    "    Y_ = 1/(1+np.exp(-(w3*x_train + b3) + 60))\n",
    "    e = Y_ - y_train\n",
    "    # 更新 w,b\n",
    "    b3 -= xuexi*np.dot(e,np.ones(m_train).transpose())/m_train\n",
    "    w3 -= xuexi*np.dot(x_train,e.transpose())/m_train\n",
    "# 绘图部分\n",
    "Y0 = 1/(1+np.exp(-(w0*x_train + b0)))\n",
    "Y1 = 1/(1+np.exp(-(w1*x_train + b1)+17.5))\n",
    "Y2 = 1/(1+np.exp(-(w2*x_train + b2)-42.5))\n",
    "Y3 = 1/(1+np.exp(-(w3*x_train + b3)+60))\n",
    "Y_ = [-1 for i in range(m_train)]\n",
    "Yx = Y1+Y2+Y3 -1 \n",
    "# lines = 60*w + b\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "plt.plot(x_train[0],Y0[0],color='k',linestyle='dashed',label='原本的',linewidth=3)\n",
    "plt.plot(x_train[0],Y1[0],color='#ec2d7a',linestyle='dashdot',label='17.5',linewidth=3)\n",
    "plt.plot(x_train[0],Y2[0],color='g',linestyle='solid',label='-42.5',linewidth=3)\n",
    "plt.plot(x_train[0],Y3[0],color='darkviolet',linestyle=(2,(1,2,3,4,2,2)),label='+60',linewidth=3)\n",
    "plt.plot(x_train[0],Y_,color='r',linestyle='solid',label='-1',linewidth=3)\n",
    "plt.plot(x_train[0],Yx[0],color='springgreen',linestyle='dotted',label='混合',linewidth=3)\n",
    "plt.scatter(x_train,y_train[0],color='b',label='原本',marker='x')\n",
    "plt.legend()\n",
    "plt.title('变化')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105aa9fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "13eef499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义ReLU函数\n",
    "def relu(x):\n",
    "    return np.maximum(0, x)\n",
    "\n",
    "# 生成一些输入数据\n",
    "x = np.linspace(-5, 5, 100)  # 生成-5到5之间的100个点作为x坐标\n",
    "\n",
    "# 计算ReLU函数的输出\n",
    "y = relu(x)\n",
    "\n",
    "# 绘制图像\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(x, y, label='ReLU(x)')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('ReLU(x)')\n",
    "plt.title('ReLU Function')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "414b5062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-18\n",
    "# 实现二分类神经网络\n",
    "# 导入酒驾数据\n",
    "# d = 5\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import random as rd\n",
    "# load dataset\n",
    "df = pandas.read_csv('alcohol_dataset.csv')\n",
    "data = np.array(df)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] -1\n",
    "# 训练次数\n",
    "epoch = 300000\n",
    "# 学习率\n",
    "XX = 0.00001\n",
    "# 最大最小归一化\n",
    "maxs = np.max(data[:,:d],0)\n",
    "mins = np.min(data[:,:d],0)\n",
    "\n",
    "data[:,:d] = (data[:,:d]-mins)/(maxs-mins)\n",
    "# mean  = np.mean(data[:,:d],0)\n",
    "# stds = np.std(data[:,:d],0,ddof=1)\n",
    "# data[:,:d] = (data[:,:d]-mean)/stds\n",
    "# 训练集测试集划分\n",
    "train_m = int(data.shape[0]*0.8)\n",
    "test_m = data.shape[0] - train_m\n",
    "train_x,train_y,test_x,test_y = data[:train_m,:d],data[:train_m,d:],data[train_m:,:d],data[train_m:,d:]\n",
    "# train_x m*d, train_y  m*1, test_x m*d\n",
    "# 设置隐含层节点数\n",
    "n = 2\n",
    "# 初始化初始权重w1  d*n和偏差 b1 n*1\n",
    "w1 = np.array([[rd.uniform(0,1) for _ in range(n)] for __ in range(d)])\n",
    "b1 = np.array([rd.uniform(0,1) for i in range(n)]).reshape(-1,1)\n",
    "# 初始化初始权重 w2 n*1 和偏差 b2 是常数\n",
    "w2 = np.array([rd.uniform(0,1) for _ in range(n)]).reshape(-1,1)\n",
    "b2 = rd.uniform(0,1)\n",
    "# 存储错误\n",
    "Errors = [[],[]]\n",
    "# 激活函数\n",
    "def ReLU(x):\n",
    "    global n\n",
    "    return np.maximum(0, x).reshape(n,-1)\n",
    "# 训练\n",
    "for _ in range(epoch):\n",
    "    # 求训练集预测值部分\n",
    "    # 第一步计算 Z1 n*m\n",
    "    Z1 = np.dot(w1.transpose(),train_x.transpose())  + np.dot(b1,np.ones(train_m).reshape(1,-1))\n",
    "    # 激活函数是ReLU 计算A1 n*m\n",
    "    A1 = ReLU(Z1)\n",
    "    # 计算Z2 1*m\n",
    "    Z2 = np.dot(w2.transpose(),A1) + b2\n",
    "    # 输出层使用的是sigmoid 激活函数 ,预测值为 predict_y\n",
    "    predict_y = 1/(1+np.exp(-Z2))\n",
    "    # 更新权重部分\n",
    "    # e 计算 m*1\n",
    "    e = predict_y.transpose() - train_y\n",
    "    predict_y ,train_y= predict_y.transpose()>=0.5,train_y >= 0.5\n",
    "    Errors[0].append(np.sum(predict_y^train_y))\n",
    "    # 更新 b1\n",
    "    b1 -= XX*w2*np.dot(A1,e)/train_m\n",
    "    # 更新 w1\n",
    "    w1 -= XX*np.dot(train_x.transpose(),((np.dot(w2,e.transpose()))*A1).transpose())\n",
    "    # 更新b2\n",
    "    b2 -= XX*np.dot(np.ones(train_m).reshape(1,-1),e)/train_m\n",
    "    # 更新 w2\n",
    "    w2 -= XX*np.dot(A1,e)/train_m\n",
    "     # 求测试集预测值部分\n",
    "    # 第一步计算 Z1 n*m\n",
    "    Z1 = np.dot(w1.transpose(),test_x.transpose())  + np.dot(b1,np.ones(test_m).reshape(1,-1))\n",
    "    # 激活函数是ReLU 计算A1 n*m\n",
    "    A1 = ReLU(Z1)\n",
    "    # 计算Z2 1*m\n",
    "    Z2 = np.dot(w2.transpose(),A1) + b2\n",
    "    # 输出层使用的是sigmoid 激活函数 ,预测值为 predict_y\n",
    "    predict_y = 1/(1+np.exp(-Z2))\n",
    "    # 更新权重部分 \n",
    "    predict_y ,test_y= predict_y.transpose()>=0.5,test_y >= 0.5\n",
    "    Errors[1].append(np.sum(predict_y^test_y))\n",
    "    \n",
    "# 绘图部分\n",
    "print(min(Errors[0]),min(Errors[1]))\n",
    "X = [i+1 for i in range(epoch)]\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "plt.plot(X,Errors[1],color='k',linestyle='dashed',label='测试集错误',linewidth=3)\n",
    "plt.plot(X,Errors[0],color='#ec2d7a',linestyle='dashdot',label='训练集错误',linewidth=3)\n",
    "plt.legend()\n",
    "plt.title('二分类神经网络')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8361aa6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2-19\n",
    "# 实现多分类神经网络\n",
    "# 1姿势正确，2坐姿偏右，3坐姿偏左，4坐姿前倾\n",
    "# d = 4\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import random as rd\n",
    "# load dataset\n",
    "df = pandas.read_csv('wheelchair_dataset.csv')\n",
    "data = np.array(df)\n",
    "data=data.astype(np.float64)\n",
    "# 随机排序\n",
    "rng = np.random.default_rng(1)\n",
    "rng.shuffle(data)\n",
    "# 输入特征维度\n",
    "d = data.shape[1] -1\n",
    "# 训练次数\n",
    "epoch = 3000\n",
    "# 学习率\n",
    "XX = 0.1\n",
    "# 最大最小归一化\n",
    "# maxs = np.max(data[:,:d],0)\n",
    "# mins = np.min(data[:,:d],0)\n",
    "# data[:,:d] = (data[:,:d]-mins)/(maxs-mins)\n",
    "mean  = np.mean(data[:,:d],0)\n",
    "stds = np.std(data[:,:d],0,ddof=1)\n",
    "data[:,:d] = (data[:,:d]-mean)/stds\n",
    "# 训练集测试集划分\n",
    "train_m = int(data.shape[0]*0.8)\n",
    "test_m = data.shape[0] - train_m\n",
    "train_x,train_y,test_x,test_y = data[:train_m,:d],data[:train_m,d:],data[train_m:,:d],data[train_m:,d:]\n",
    "train_y,test_y = train_y.transpose(),test_y.transpose()\n",
    "train_y,test_y = np.array([train_y[0]==1.,train_y[0]==2.,train_y[0]==3.,train_y[0]==4.]),np.array([test_y[0]==1.,test_y[0]==2.,test_y[0]==3.,test_y[0]==4.])\n",
    "# train_x m*d, train_y  c*m, test_x m*d\n",
    "# 设置隐含层节点数\n",
    "n = 3\n",
    "# 初始化初始权重w1  d*n和偏差 b1 n*1\n",
    "w1 = np.array([[rd.uniform(0,1) for _ in range(n)] for __ in range(d)])\n",
    "b1 = np.array([rd.uniform(0,1) for i in range(n)]).reshape(-1,1)\n",
    "# 初始化初始权重 w2 n*c 和偏差 b2 c*1\n",
    "w2 = np.array([[rd.uniform(0,1) for _ in range(4)] for __ in range(n)])\n",
    "b2 = np.array([rd.uniform(0,1) for i in range(4)]).reshape(-1,1)\n",
    "# 存储错误\n",
    "Errors = [[],[]]\n",
    "# 激活函数\n",
    "def ReLU(x):\n",
    "    global n\n",
    "    return np.maximum(0, x).reshape(n,-1)\n",
    "# 训练\n",
    "for _ in range(epoch):\n",
    "    # 求训练集预测值部分\n",
    "    # 第一步计算 Z1 n*m\n",
    "    Z1 = np.dot(w1.transpose(),train_x.transpose())  + np.dot(b1,np.ones(train_m).reshape(1,-1))\n",
    "    # 激活函数是ReLU 计算A1 n*m\n",
    "    A1 = ReLU(Z1)\n",
    "    # 计算Z2 c*m\n",
    "    Z2 = np.dot(w2.transpose(),A1) + np.dot(b2,np.ones(train_m).reshape(1,-1))\n",
    "    # 输出层使用的是sigmoid 激活函数 ,预测值为 predict_y\n",
    "    predict_y = np.exp(Z2)/np.dot(np.ones(4*4).reshape(4,4),np.exp(Z2))\n",
    "    # 更新权重部分\n",
    "    # E 计算 c*m\n",
    "    E = predict_y - train_y\n",
    "    # 统计错误\n",
    "    e = np.argmax(predict_y,0)-np.argmax(train_y,0)\n",
    "    Errors[0].append(np.sum(e*e))\n",
    "    # 更新 b1\n",
    "#     print('w2',w2.shape,'E',E.shape,'A1',A1.shape)\n",
    "    b1 -= XX*np.dot(np.dot(w2,E)*A1,np.ones(train_m).reshape(-1,1))/train_m\n",
    "    # 更新 w1\n",
    "    w1 -= XX*np.dot(train_x.transpose(),(np.dot(w2,E)*A1).transpose())/train_m\n",
    "    # 更新b2\n",
    "    b2 -= XX*np.dot(E,np.ones(train_m).reshape(-1,1))/train_m\n",
    "    # 更新 w2\n",
    "    w2 -= XX*np.dot(A1,E.transpose())/train_m\n",
    "     # 求测试集预测值部分\n",
    "    Z1 = np.dot(w1.transpose(),test_x.transpose())  + np.dot(b1,np.ones(test_m).reshape(1,-1))\n",
    "    # 激活函数是ReLU 计算A1 n*m\n",
    "    A1 = ReLU(Z1)\n",
    "    # 计算Z2 c*m\n",
    "    Z2 = np.dot(w2.transpose(),A1) + np.dot(b2,np.ones(test_m).reshape(1,-1))\n",
    "    # 输出层使用的是sigmoid 激活函数 ,预测值为 predict_y\n",
    "    predict_y = np.exp(Z2)/np.dot(np.ones(4*4).reshape(4,4),Z2)\n",
    "    # 更新权重部分\n",
    "    # 统计错误\n",
    "    e = np.argmax(predict_y,0)-np.argmax(test_y,0)\n",
    "    Errors[1].append(np.sum(e*e))\n",
    "    \n",
    "# 绘图部分\n",
    "print(min(Errors[0]),min(Errors[1]))\n",
    "X = [i+1 for i in range(epoch)]\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "plt.plot(X,Errors[1],color='k',linestyle='dashed',label='测试集错误',linewidth=3)\n",
    "plt.plot(X,Errors[0],color='#ec2d7a',linestyle='dashdot',label='训练集错误',linewidth=3)\n",
    "plt.legend()\n",
    "plt.title('多分类神经网络')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e91b0fab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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